Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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4,700 | 5,256 | Parallel Direction Method of Multipliers
Huahua Wang , Arindam Banerjee , Zhi-Quan Luo
University of Minnesota, Twin Cities
{huwang,banerjee}@cs.umn.edu, luozq@umn.edu
Abstract
We consider the problem of minimizing block-separable (non-smooth) convex
functions subject to linear constraints. While the Alternating Dire... | 5256 |@word version:3 norm:1 open:2 tried:1 decomposition:4 hsieh:1 pick:2 cyclic:1 substitution:2 liu:1 zij:1 tuned:1 interestingly:2 existing:2 current:1 luo:7 chu:1 written:1 numerical:1 j1:3 nonnegativeness:1 plot:1 update:39 kxjt:1 xk:4 fa9550:1 provides:1 iterates:2 successive:2 atj:1 along:2 mathematical:1 direc... |
4,701 | 5,257 | Constant Nullspace Strong Convexity and
Fast Convergence of Proximal Methods under
High-Dimensional Settings
Ian E.H. Yen
Cho-Jui Hsieh
Pradeep Ravikumar
Inderjit Dhillon
Department of Computer Science
University of Texas at Austin
{ianyen,cjhsieh,pradeepr,inderjit}@cs.utexas.edu
Abstract
State of the art statistica... | 5257 |@word version:1 stronger:2 norm:20 d2:4 hsieh:3 covariance:2 liblinear:1 nesta:1 document:1 ati:3 past:1 nt:4 luo:2 yet:1 written:3 must:1 hou:1 numerical:2 happen:1 update:6 greedy:1 core:1 caveat:1 math:1 zhang:1 along:1 qualitative:1 prove:4 yuan:1 introductory:1 polyhedral:3 introduce:4 manner:1 bobin:1 x0:3 ... |
4,702 | 5,258 | SAGA: A Fast Incremental Gradient Method With
Support for Non-Strongly Convex Composite
Objectives
Francis Bach
INRIA - Sierra Project-Team
?
Ecole
Normale Sup?erieure, Paris, France
Aaron Defazio
Ambiata ?
Australian National University, Canberra
Simon Lacoste-Julien
INRIA - Sierra Project-Team
?
Ecole
Normale Sup?... | 5258 |@word msr:1 briefly:1 version:3 eliminating:1 middle:1 decomposition:3 pick:3 sgd:3 mention:1 reduction:8 initial:2 series:1 pub:1 ecole:2 past:1 existing:1 current:1 comparing:1 com:1 yet:1 readily:1 update:29 v:1 xk:68 beginning:1 ith:1 draft:1 location:1 firstly:1 zhang:6 prove:2 combine:1 introductory:1 insid... |
4,703 | 5,259 | Time?Data Tradeoffs by Aggressive Smoothing
John J. Bruer1,*
Joel A. Tropp1
Volkan Cevher2
Stephen R. Becker3
1 Dept. of Computing + Mathematical Sciences, California Institute of Technology
2 Laboratory for Information and Inference Systems, EPFL
3 Dept. of Applied Mathematics, University of Colorado at Boulder
* jbr... | 5259 |@word trial:3 version:1 inversion:1 norm:17 d2:1 linearized:2 nemirovsky:1 covariance:1 decomposition:1 automat:1 reap:1 reduction:1 current:2 optim:2 must:4 written:1 john:2 numerical:6 plot:2 v:4 fewer:2 selected:1 xk:6 fa9550:1 volkan:1 provides:4 characterization:2 iterates:4 completeness:1 math:5 height:1 ma... |
4,704 | 526 | Oscillatory Model of Short Term Memory
David Horn
School of Physics and Astronomy
Raymond and Beverly Sackler
Faculty of Exact Sciences
Tel-Aviv University
Tel Aviv 69978, Israel
Marius U sher*
Dept. of Applied Mathematics
and Computer Science
Weizmann Institute of Science
Rehovot 76100, Israel
Abstract
We investiga... | 526 |@word version:1 faculty:1 seems:2 r:2 simplifying:1 minus:1 carry:1 contains:2 exclusively:1 activation:3 yet:1 reminiscent:1 numerical:1 plasticity:1 alone:1 item:4 short:12 compo:5 indefinitely:1 mathematical:1 along:1 c2:2 differential:1 sustained:1 recognizable:1 indeed:1 behavior:7 brain:1 prolonged:1 endless... |
4,705 | 5,260 | Local Linear Convergence of Forward?Backward
under Partial Smoothness
Jingwei Liang and Jalal M. Fadili
GREYC, CNRS-ENSICAEN-Univ. Caen
{Jingwei.Liang,Jalal.Fadili}@greyc.ensicaen.fr
Gabriel Peyr?
CEREMADE, CNRS-Univ. Paris-Dauphine
Gabriel.Peyre@ceremade.dauphine.fr
Abstract
In this paper, we consider the Forward?Ba... | 5260 |@word mild:1 version:1 norm:48 open:1 simulation:1 decomposition:2 series:3 hereafter:1 ours:2 yet:1 numerical:1 update:1 rudin:1 xk:117 ptm:1 iterates:1 math:2 zhang:1 mathematical:1 along:1 become:1 yuan:1 polyhedral:5 x0:22 cand:2 dist:2 sdp:1 becomes:3 provided:1 notation:1 moreover:5 elser:1 argmin:4 string:... |
4,706 | 5,261 | Simple MAP Inference via Low-Rank Relaxations
Roy Frostig?, Sida I. Wang,? Percy Liang, Christopher D. Manning
Computer Science Department, Stanford University, Stanford, CA, 94305
{rf,sidaw,pliang}@cs.stanford.edu, manning@stanford.edu
Abstract
We focus on the problem of maximum a posteriori (MAP) inference in Markov... | 5261 |@word kulis:1 illustrating:1 polynomial:1 advantageous:1 norm:4 scalably:1 decomposition:1 paid:2 asks:1 incurs:1 thereby:2 tr:6 reduction:2 initial:1 celebrated:1 configuration:1 score:2 sherali:1 tuned:1 outperforms:1 existing:1 current:1 must:3 gpu:1 analytic:1 plot:1 drop:2 update:18 treating:1 v:2 intelligen... |
4,707 | 5,262 | Inferring synaptic conductances from spike trains
under a biophysically inspired point process model
E. J. Chichilnisky
Department of Neurosurgery
Hansen Experimental Physics Laboratory
Stanford University
ej@stanford.edu
Kenneth W. Latimer
The Institute for Neuroscience
The University of Texas at Austin
latimerk@ute... | 5262 |@word trial:5 norm:2 hippocampus:1 simulation:5 simplifying:1 thereby:1 solid:1 initial:1 selecting:1 tuned:1 current:4 must:1 realistic:3 numerical:1 hyperpolarizing:1 informative:1 shape:1 motor:1 plot:1 interpretable:2 v:5 alone:1 generative:1 selected:1 filtered:1 characterization:2 provides:2 psth:2 five:2 h... |
4,708 | 5,263 | Low-dimensional models of neural population activity
in sensory cortical circuits
Evan Archer1,2 , Urs K?oster3 , Jonathan Pillow4 , Jakob H. Macke1,2
1
Max Planck Institute for Biological Cybernetics, T?ubingen
2
Bernstein Center for Computational Neuroscience, T?ubingen
3
Redwood Center for Theoretical Neuroscience,... | 5263 |@word neurophysiology:1 trial:6 illustrating:1 briefly:1 private:1 wiesel:1 loading:1 seek:2 simulation:2 covariance:7 thereby:1 tr:1 harder:1 reduction:1 series:1 past:1 imaginary:2 current:2 com:1 recovered:5 blank:1 realize:1 physiol:1 realistic:1 subsequent:1 shape:1 analytic:1 enables:1 mstep:1 treating:1 wa... |
4,709 | 5,264 | Inferring sparse representations of continuous signals
with continuous orthogonal matching pursuit
Jacob L. Yates
Department of Neuroscience
The University of Texas at Austin
jlyates@utexas.edu
Karin C. Knudson
Department of Mathematics
The University of Texas at Austin
kknudson@math.utexas.edu
Alexander C. Huk
Cente... | 5264 |@word trial:1 milenkovic:1 version:1 middle:1 eliminating:1 norm:1 blu:1 seek:6 simulation:3 jacob:1 decomposition:5 dramatic:1 solid:2 series:1 selecting:2 daniel:2 recovered:10 discretization:2 com:1 must:2 fn:36 shape:1 enables:1 update:6 discrimination:1 greedy:14 afn:4 ith:1 iterates:2 math:1 cbp:47 rc:3 c2:... |
4,710 | 5,265 | Fast Sampling-Based Inference in Balanced Neuronal
Networks
Guillaume Hennequin1
gjeh2@cam.ac.uk
Laurence Aitchison2
laurence@gatsby.ucl.ac.uk
M?at?e Lengyel1
m.lengyel@eng.cam.ac.uk
1
2
Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK
Gatsby Computational Neuroscience Unit,... | 5265 |@word private:1 middle:3 version:1 determinant:1 laurence:2 seems:1 norm:2 inversion:1 open:1 grey:1 simulation:4 seek:2 linearized:1 eng:1 covariance:15 dramatic:1 solid:3 carry:1 moment:3 series:2 contains:1 si:12 yet:2 must:7 additive:1 numerical:2 plasticity:1 shape:1 motor:1 moreno:1 update:3 stationary:7 ge... |
4,711 | 5,266 | Information-based learning by agents in unbounded
state spaces
Shariq A. Mobin, James A. Arnemann, Friedrich T. Sommer
Redwood Center for Theoretical Neuroscience
University of California, Berkeley
Berkeley, CA 94720
shariqmobin@berkeley.edu, arnemann@berkeley.edu, fsommer@berkeley.edu
Abstract
The idea that animals ... | 5266 |@word nihat:1 exploitation:1 version:3 polynomial:1 simulation:2 citeseer:1 euclidian:1 accommodate:1 jacqueline:1 harder:1 initial:1 series:2 selecting:1 daniel:2 ecole:1 document:1 undiscovered:4 interestingly:3 outperforms:1 existing:1 current:6 comparing:3 manuel:1 yet:2 assigning:1 must:4 john:1 explorative:... |
4,712 | 5,267 | On the Computational Efficiency of Training Neural
Networks
Roi Livni
The Hebrew University
roi.livni@mail.huji.ac.il
Shai Shalev-Shwartz
The Hebrew University
shais@cs.huji.ac.il
Ohad Shamir
Weizmann Institute of Science
ohad.shamir@weizmann.ac.il
Abstract
It is well-known that neural networks are computationally h... | 5267 |@word version:1 dalal:1 polynomial:42 advantageous:1 norm:2 nd:1 triggs:1 d2:1 tried:2 bn:2 pick:1 sgd:12 reduction:1 contains:4 existing:1 com:1 nt:16 activation:43 must:1 devin:1 happen:1 plot:2 greedy:9 half:2 advancement:1 intelligence:1 warmuth:1 caveat:1 provides:3 node:1 sigmoidal:9 zhang:2 unbounded:1 dir... |
4,713 | 5,268 | Attentional Neural Network: Feature Selection Using
Cognitive Feedback
Qian Wang
Department of Biomedical Engineering
Tsinghua University
Beijing, China 100084
qianwang.thu@gmail.com
Jiaxing Zhang
Microsoft Research Asia
5 Danning Road, Haidian District
Beijing, China 100080
jiaxz@microsoft.com
Sen Song ?
Department... | 5268 |@word version:2 seems:2 confirms:1 propagate:1 git:1 pick:3 harder:1 accommodate:3 initial:3 series:2 score:2 selecting:2 interestingly:1 past:1 existing:1 err:3 current:2 com:4 comparing:2 segmentaion:1 contextual:1 activation:3 gmail:2 yet:2 reminiscent:1 happen:1 partition:1 confirming:1 cheap:2 hypothesize:1 ... |
4,714 | 5,269 | Expectation Backpropagation: Parameter-Free
Training of Multilayer Neural Networks with
Continuous or Discrete Weights
Daniel Soudry1 , Itay Hubara2 , Ron Meir2
(1) Department of Statistics, Columbia University
(2) Department of Electrical Engineering, Technion, Israel Institute of Technology
daniel.soudry@gmail.com,i... | 5269 |@word version:2 polynomial:2 nd:1 nonsensical:1 open:1 km:5 covariance:1 simplifying:1 recursively:2 initial:1 configuration:5 contains:1 daniel:2 document:1 interestingly:2 outperforms:2 com:2 discretization:1 activation:9 gmail:2 dx:2 must:3 numerical:3 analytic:1 update:18 fund:1 discrimination:1 v:2 intellige... |
4,715 | 527 | Improved Hidden Markov Model
Speech Recognition Using
Radial Basis Function Networks
Elliot Singer and Richard P. Lippmann
Lincoln Laboratory, MIT
Lexington, MA 02173-9108, USA
Abstract
A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMM... | 527 |@word briefly:1 inversion:2 pw:1 lwk:1 seek:1 covariance:1 tr:1 initial:2 score:4 must:1 john:1 numerical:1 designed:2 plot:1 update:1 sponsored:1 discrimination:1 fewer:2 selected:1 ith:4 short:1 provides:2 node:21 ron:1 cse:1 sigmoidal:1 nodal:3 consists:2 combine:2 kenney:1 expected:1 behavior:1 actual:1 window... |
4,716 | 5,270 | An Autoencoder Approach to Learning
Bilingual Word Representations
Sarath Chandar A P1 ? , Stanislas Lauly2 ? , Hugo Larochelle2 , Mitesh M Khapra3 ,
Balaraman Ravindran1 , Vikas Raykar3 , Amrita Saha3
1
Indian Institute of Technology Madras, 2 Universit?e de Sherbrooke, 3 IBM Research India
apsarathchandar@gmail.com,... | 5270 |@word multitask:1 trial:1 exploitation:1 briefly:1 nd:2 simplifying:1 decomposition:2 pavel:1 mention:2 tr:6 yih:3 cyclic:1 contains:2 liu:1 daniel:1 tuned:1 document:32 past:1 outperforms:3 subjective:1 com:2 z2:1 comparing:1 activation:1 gmail:1 lang:2 must:3 readily:1 lauly:3 written:1 john:3 parsing:1 ronan:1... |
4,717 | 5,271 | Pre-training of Recurrent Neural Networks via
Linear Autoencoders
Luca Pasa, Alessandro Sperduti
Department of Mathematics
University of Padova, Italy
{pasa,sperduti}@math.unipd.it
Abstract
We propose a pre-training technique for recurrent neural networks based on linear
autoencoder networks for sequences, i.e. linear... | 5271 |@word trial:1 longterm:1 norm:1 seems:1 open:1 r:2 bn:1 decomposition:17 covariance:5 initial:6 initialisation:4 outperforms:2 recovered:1 current:2 com:1 must:1 gpu:1 xb1:1 polyphonic:5 v:2 devising:1 pasa:2 vanishing:2 core:1 math:1 sigmoidal:2 simpler:1 zhang:1 mathematical:1 direct:1 become:1 consists:1 insid... |
4,718 | 5,272 | Using Convolutional Neural Networks
to Recognize Rhythm Stimuli
from Electroencephalography Recordings
Sebastian Stober, Daniel J. Cameron and Jessica A. Grahn
Brain and Mind Institute, Department of Psychology, Western University
London, Ontario, Canada, N6A 5B7
{sstober,dcamer25,jgrahn}@uwo.ca
Abstract
Electroencep... | 5272 |@word neurophysiology:2 trial:13 cnn:7 mri:1 polynomial:3 replicate:1 nd:1 open:1 pick:2 sgd:1 minus:1 harder:4 initial:3 configuration:6 score:1 daniel:1 tuned:1 africa:3 com:3 activation:1 dx:2 written:1 gpu:1 grain:1 realistic:1 partition:1 shape:2 designed:1 plot:1 v:3 discrimination:3 grass:1 selected:2 fewe... |
4,719 | 5,273 | Neurons as Monte Carlo Samplers: Bayesian
Inference and Learning in Spiking Networks
Rajesh P.N. Rao
University of Washington
rao@cs.uw.edu
Yanping Huang
University of Washington
huangyp@cs.uw.edu
Abstract
We propose a spiking network model capable of performing both approximate
inference and learning for any hidden... | 5273 |@word mild:1 trial:8 version:1 briefly:1 norm:1 open:2 proportionality:1 simulation:1 sensed:1 eng:1 pressure:1 solid:5 recursively:1 initial:10 contains:2 njk:8 freitas:1 current:3 comparing:1 must:1 numerical:1 plasticity:1 motor:1 plot:1 treating:1 update:3 alone:1 half:1 stationary:1 nervous:1 xk:36 ith:1 pau... |
4,720 | 5,274 | A framework for studying synaptic plasticity
with neural spike train data
Scott W. Linderman
Harvard University
Cambridge, MA 02138
Christopher H. Stock
Harvard College
Cambridge, MA 02138
Ryan P. Adams
Harvard University
Cambridge, MA 02138
swl@seas.harvard.edu
cstock@post.harvard.edu
rpa@seas.harvard.edu
Abstr... | 5274 |@word neurophysiology:1 trial:1 middle:2 interleave:1 open:1 simulation:2 propagate:2 bn:3 dramatic:1 fifteen:1 accommodate:1 deisseroth:1 initial:1 series:3 daniel:3 hirtz:1 outperforms:2 existing:1 current:7 anne:1 written:3 must:4 john:2 additive:17 visible:3 plasticity:31 interspike:1 update:9 n0:21 stationar... |
4,721 | 5,275 | Global Belief Recursive Neural Networks
Romain Paulus, Richard Socher?
MetaMind
Palo Alto, CA
{romain,richard}@metamind.io
Christopher D. Manning
Stanford University
353 Serra Mall
Stanford, CA 94305
manning@stanford.edu
Abstract
Recursive Neural Networks have recently obtained state of the art performance on
several... | 5275 |@word bigram:4 nd:14 propagate:1 simplifying:1 yih:1 recursively:2 reduction:1 series:3 score:5 tuned:2 document:4 ours:3 outperforms:2 past:1 current:1 contextual:8 activation:3 parsing:3 stemming:1 concatenate:1 update:2 alone:1 intelligence:2 leaf:3 selected:1 slowing:1 smith:1 short:2 node:32 lexicon:4 monday... |
4,722 | 5,276 | Deep Networks with Internal Selective Attention
through Feedback Connections
Marijn F. Stollenga?, Jonathan Masci? , Faustino Gomez, Juergen Schmidhuber
IDSIA, USI-SUPSI
Manno-Lugano, Switzerland
{marijn,jonathan,tino,juergen}@idsia.ch
Abstract
Traditional convolutional neural networks (CNN) are stationary and feedfor... | 5276 |@word trial:2 cnn:10 compression:2 open:1 shuicheng:1 schomaker:1 covariance:3 systeme:1 thereby:1 tr:1 shot:1 initial:3 contains:1 score:1 selecting:1 united:1 foveal:1 outperforms:1 freitas:1 current:2 activation:16 yet:1 must:3 parsing:1 gpu:1 confirming:1 localise:1 drop:2 progressively:1 update:2 v:1 station... |
4,723 | 5,277 | Iterative Neural Autoregressive Distribution
Estimator (NADE-k)
Tapani Raiko
Aalto University
Li Yao
Universit?e de Montr?eal
KyungHyun Cho
Universit?e de Montr?eal
Yoshua Bengio
Universit?e de Montr?eal,
CIFAR Senior Fellow
Abstract
Training of the neural autoregressive density estimator (NADE) can be viewed as
d... | 5277 |@word version:4 middle:2 seems:2 reused:1 confirms:1 git:2 contrastive:1 tr:1 harder:1 series:1 tuned:1 yaoli:1 outperforms:5 freitas:1 com:1 od:1 varx:1 activation:4 gpu:1 uria:11 subsequent:1 happen:1 visible:1 drop:1 update:2 resampling:1 generative:6 half:1 selected:2 intelligence:2 core:1 short:1 regressive:... |
4,724 | 5,278 | General Stochastic Networks for Classification
Matthias Z?ohrer and Franz Pernkopf
Signal Processing and Speech Communication Laboratory
Graz University of Technology
matthias.zoehrer@tugraz.at, pernkopf@tugraz.at
Abstract
We extend generative stochastic networks to supervised learning of representations. In particula... | 5278 |@word middle:3 proportion:1 norm:4 nd:1 seems:1 open:2 propagate:1 covariance:1 contrastive:1 sgd:2 mention:1 generatively:1 configuration:1 denoting:1 document:1 outperforms:1 recovered:1 comparing:2 com:1 activation:7 bd:1 written:1 gpu:2 treating:1 designed:1 update:9 generative:21 greedy:2 intelligence:2 begi... |
4,725 | 5,279 | Improved Multimodal Deep Learning
with Variation of Information
Kihyuk Sohn, Wenling Shang and Honglak Lee
University of Michigan Ann Arbor, MI, USA
{kihyuks,shangw,honglak}@umich.edu
Abstract
Deep learning has been successfully applied to multimodal representation learning problems, with a common strategy to learning... | 5279 |@word cnn:1 faculty:1 version:1 stronger:1 seems:1 r:1 rgb:2 crbms:1 contrastive:4 datagenerating:1 sgd:2 initial:2 score:1 selecting:1 document:1 outperforms:2 com:1 od:1 activation:1 written:4 concatenate:1 visible:6 wx:3 shape:1 enables:1 gist:1 update:9 generative:11 half:4 website:2 greedy:1 intelligence:1 y... |
4,726 | 528 | Direction Selective Silicon Retina
that uses N uIl Inhibition
Ronald G. Benson and Tobi Delbriick
Computation and Neural Systems Program, 139-74
California Institute of Technology
Pasadena CA 91125
email: benson@cns.caltech.edu and tdelbruck@caltech.edu
Abstract
Biological retinas extract spatial and temporal feature... | 528 |@word compression:1 pulse:6 excited:1 blade:2 electronics:1 series:1 tuned:5 past:1 existing:1 current:3 john:1 fn:1 physiol:1 ronald:1 visible:3 visibility:1 designed:3 plot:1 reciprocal:1 characterization:2 quantized:1 node:1 symposium:1 consists:2 pathway:2 expected:1 behavior:2 abscissa:1 retard:1 detects:1 ve... |
4,727 | 5,280 | Restricted Boltzmann machines modeling human
choice
Makoto Otsuka
IBM Research - Tokyo
motsuka@ucla.edu
Takayuki Osogami
IBM Research - Tokyo
osogami@jp.ibm.com
Abstract
We extend the multinomial logit model to represent some of the empirical phenomena that are frequently observed in the choices made by humans. These... | 5280 |@word logit:2 nd:1 simulation:1 solid:1 initial:1 configuration:4 att:3 selecting:1 exclusively:1 existing:2 freitas:1 com:4 yet:1 must:3 readily:1 pioneer:1 cruz:1 mesh:1 visible:9 numerical:2 wx:1 john:1 uak:4 shape:1 visibility:1 rieskamp:3 intelligence:1 selected:8 item:25 realizing:2 core:2 node:1 preference... |
4,728 | 5,281 | Deep Fragment Embeddings for Bidirectional Image
Sentence Mapping
Andrej Karpathy
Armand Joulin
Li Fei-Fei
Department of Computer Science, Stanford University, Stanford, CA 94305, USA
{karpathy,ajoulin,feifeili}@cs.stanford.edu
Abstract
We introduce a model for bidirectional retrieval of images and sentences through
... | 5281 |@word multitask:1 armand:1 cnn:21 bigram:5 c0:6 open:1 sgd:1 dramatic:1 mention:3 contains:2 fragment:78 score:33 salzmann:1 document:3 outperforms:2 freitas:1 comparing:1 gauvain:1 activation:3 must:1 written:1 gpu:1 parsing:2 happen:1 hofmann:1 remove:1 treating:1 interpretable:3 drop:4 alone:1 intelligence:1 i... |
4,729 | 5,282 | Recursive Context Propagation Network for Semantic
Scene Labeling
Abhishek Sharma
University of Maryland
College Park, MD
bhokaal@cs.umd.edu
Oncel Tuzel
Ming-Yu Liu
Mitsubishi Electric Research Labs (MERL)
Cambridge, MA
{oncel,mliu}@merl.com
Abstract
We propose a deep feed-forward neural network architecture for pix... | 5282 |@word cnn:43 middle:1 inversion:1 open:1 mitsubishi:1 rgb:4 propagate:7 twolayer:1 thereby:1 recursively:10 liu:6 configuration:1 contains:3 selecting:2 daniel:1 interestingly:3 outperforms:1 past:1 existing:2 com:3 contextual:12 gpu:9 parsing:8 john:1 ronan:1 hofmann:1 designed:6 drop:1 update:3 grass:2 v:2 alon... |
4,730 | 5,283 | Multiscale Fields of Patterns
John G. Oberlin
Brown University
Providence, RI 02906
john oberlin@brown.edu
Pedro F. Felzenszwalb
Brown University
Providence, RI 02906
pff@brown.edu
Abstract
We describe a framework for defining high-order image models that can be used
in a variety of applications. The approach involve... | 5283 |@word kohli:1 illustrating:1 nd:2 jacob:2 pick:1 brightness:1 inpainting:1 initial:1 configuration:5 contains:1 selecting:1 suppressing:1 rowan:1 current:2 assigning:1 john:2 realistic:1 shape:7 treating:1 update:8 stationary:1 cue:1 selected:1 leaf:4 parameterization:1 inspection:1 xk:17 core:2 accepting:1 coars... |
4,731 | 5,284 | Weakly-supervised Discovery of
Visual Pattern Configurations
Hyun Oh Song
Yong Jae Lee*
Stefanie Jegelka
*
University of California, Berkeley
Trevor Darrell
University of California, Davis
Abstract
The prominence of weakly labeled data gives rise to a growing demand for object detection methods that can cope wit... | 5284 |@word kohli:1 cnn:3 tedious:1 prominence:1 contrastive:1 pick:1 locomotive:1 thereby:1 solid:2 initial:4 configuration:44 contains:3 fragment:1 score:2 efficacy:1 ours:2 existing:2 activation:1 must:3 reminiscent:1 realistic:1 happen:1 informative:7 partition:2 shape:1 visible:1 update:5 greedy:6 fewer:1 guess:2 ... |
4,732 | 5,285 | Encoding High Dimensional Local Features by Sparse
Coding Based Fisher Vectors
Lingqiao Liu1 , Chunhua Shen1,2 , Lei Wang3 , Anton van den Hengel1,2 , Chao Wang3
1
School of Computer Science, University of Adelaide, Australia
2
ARC Centre of Excellence for Robotic Vision
3
School of Computer Science and Software Engin... | 5285 |@word cnn:35 middle:2 open:1 tried:1 reduction:1 necessity:1 series:1 contains:3 outperforms:8 com:2 activation:7 gmail:1 written:1 readily:2 concatenate:1 partition:8 designed:1 drop:1 depict:1 v:2 generative:6 fvc:2 es:1 provides:1 codebook:3 gx:2 firstly:1 org:1 zhang:2 become:1 excellence:1 roughly:1 examine:... |
4,733 | 5,286 | Self-Adaptable Templates for Feature Coding
Xavier Boix1,2? Gemma Roig1,2? Salomon Diether1 Luc Van Gool1
1
Computer Vision Laboratory, ETH Zurich, Switzerland
2
LCSL, Massachusetts Institute of Technology & Istituto Italiano di Tecnologia, Cambridge, MA
{xboix,gemmar}@mit.edu
{boxavier,gemmar,sdiether,vangool}@vision... | 5286 |@word cox:1 version:1 norm:1 everingham:1 bf:6 covariance:2 hsieh:1 tr:6 shot:1 liblinear:2 celebrated:1 contains:3 configuration:1 selecting:1 score:1 batista:1 interestingly:1 comparing:2 surprising:1 yet:3 intriguing:1 assigning:1 written:3 concatenate:2 depict:1 selected:1 yr:1 potted:1 quantized:1 codebook:8... |
4,734 | 5,287 | Learning From Weakly Supervised Data by The
Expectation Loss SVM (e-SVM) algorithm
Junhua Mao
Department of Statistics
University of California, Los Angeles
mjhustc@ucla.edu
Jun Zhu
Department of Statistics
University of California, Los Angeles
jzh@ucla.edu
Alan Yuille
Department of Statistics
University of Californ... | 5287 |@word cnn:2 middle:1 briefly:2 everingham:2 rgb:1 hsieh:1 liblinear:2 liu:3 series:1 score:14 tuned:1 ours:4 batista:1 outperforms:3 existing:1 current:1 si:4 parsing:1 readily:1 concatenate:2 additive:3 hofmann:1 motor:1 treating:2 ainen:1 v:1 cue:6 selected:1 plane:1 smith:1 potted:1 firstly:2 org:3 direct:2 be... |
4,735 | 5,288 | Predicting Useful Neighborhoods
for Lazy Local Learning
Kristen Grauman
University of Texas at Austin
grauman@cs.utexas.edu
Aron Yu
University of Texas at Austin
aron.yu@utexas.edu
Abstract
Lazy local learning methods train a classifier ?on the fly? at test time, using only
a subset of the training instances that ar... | 5288 |@word kulis:1 version:1 compression:1 retraining:1 nd:1 liu:2 series:5 contains:2 selecting:1 hoiem:1 shum:1 ours:13 document:1 envision:1 rightmost:2 yni:10 existing:6 xnj:1 recovered:1 past:1 outperforms:1 yet:3 must:4 distant:2 informative:3 kdd:1 shape:1 hofmann:1 treating:1 v:1 alone:3 selected:6 discovering... |
4,736 | 5,289 | A Unified Semantic Embedding:
Relating Taxonomies and Attributes
Sung Ju Hwang?
Disney Research
Pittsburgh, PA
sungju.hwang@disneyresearch.com
Leonid Sigal
Disney Research
Pittsburgh, PA
lsigal@disneyresearch.com
Abstract
We propose a method that learns a discriminative yet semantic space for object
categorization, w... | 5289 |@word multitask:8 norm:6 nd:1 hu:1 r:2 llo:1 decomposition:8 mammal:2 shot:8 ld:1 contains:1 paw:2 hoiem:1 ours:1 document:1 existing:3 com:2 nt:1 si:1 yet:1 activation:1 john:1 numerical:1 shape:1 enables:3 musteline:2 flipper:4 zaid:1 interpretable:2 discrimination:4 generative:8 leaf:1 selected:2 intelligence:... |
4,737 | 529 | Neural Computing with Small Weights
Kai-Yeung Siu
Dept. of Electrical & Computer Engineering
University of California, Irvine
Irvine, CA 92717
J ehoshua Bruck
IBM Research Division
Almaden Research Center
San Jose, CA 95120-6099
Abstract
An important issue in neural computation is the dynamic range of weights
in the... | 529 |@word toda:1 indicate:2 polynomial:21 norm:8 come:1 implies:1 hence:2 question:2 open:1 closely:1 maass:1 simulation:2 sgn:5 raghavan:1 ll:7 mention:1 require:2 simulated:4 sci:3 it1:1 majority:3 generalized:1 yajima:1 tt:1 aes:1 pro:1 com:3 wdi:1 schnitger:1 harmonic:8 recently:2 must:2 fi:2 aco:1 iwil:1 october:... |
4,738 | 5,290 | Zero-Shot Recognition with Unreliable Attributes
Kristen Grauman
University of Texas at Austin
Austin, TX 78701
grauman@cs.utexas.edu
Dinesh Jayaraman
University of Texas at Austin
Austin, TX 78701
dineshj@cs.utexas.edu
Abstract
In principle, zero-shot learning makes it possible to train a recognition model
simply b... | 5290 |@word trial:1 illustrating:1 achievable:1 proportion:2 norm:1 seal:1 propagate:3 accounting:3 xtest:6 attainable:1 concise:1 dramatic:1 accommodate:1 shot:64 recursively:4 initial:2 configuration:1 quo:1 score:2 selecting:3 hoiem:1 ours:13 interestingly:1 outperforms:2 existing:7 must:3 fn:2 gavves:1 j1:1 shape:1... |
4,739 | 5,291 | Articulated Pose Estimation by a Graphical Model
with Image Dependent Pairwise Relations
Alan Yuille
University of California, Los Angeles
Los Angeles, CA 90024
yuille@stat.ucla.edu
Xianjie Chen
University of California, Los Angeles
Los Angeles, CA 90024
cxj@ucla.edu
Abstract
We present a method for estimating artic... | 5291 |@word version:3 briefly:1 dalal:1 stronger:1 norm:3 everingham:1 triggs:1 open:1 recursively:1 reduction:1 configuration:3 contains:5 score:11 liu:1 jimenez:2 ours:8 outperforms:7 recovered:1 contextual:1 si:2 chu:1 written:1 must:1 pcp:17 informative:1 midway:2 hofmann:1 enables:1 newest:1 cue:1 leaf:1 alone:1 i... |
4,740 | 5,292 | Exact Post Model Selection Inference for Marginal
Screening
Jason D. Lee
Computational and Mathematical Engineering
Stanford University
Stanford, CA 94305
jdl17@stanford.edu
Jonathan E. Taylor
Department of Statistics
Stanford University
Stanford, CA 94305
jonathan.taylor@stanford.edu
Abstract
We develop a framework... | 5292 |@word trial:2 norm:1 proportion:9 sex:1 unif:4 elisseeff:1 pressure:1 contains:1 series:1 selecting:1 daniel:1 bootstrapped:1 bradley:1 com:1 nicolai:1 chu:1 must:1 belmont:1 partition:4 plot:3 v:3 greedy:2 selected:17 tscher:3 core:1 jiashun:1 provides:1 bijection:2 zhang:3 mathematical:2 constructed:7 become:1 ... |
4,741 | 5,293 | On Iterative Hard Thresholding Methods for
High-dimensional M-Estimation
Prateek Jain?
Ambuj Tewari?
Purushottam Kar?
Microsoft Research, INDIA
?
University of Michigan, Ann Arbor, USA
{prajain,t-purkar}@microsoft.com, tewaria@umich.edu
?
Abstract
The use of M-estimators in generalized linear regression models in hi... | 5293 |@word milenkovic:1 version:2 polynomial:1 norm:2 stronger:1 c0:2 r:23 crucially:1 git:3 covariance:3 contraction:1 decomposition:2 simulation:1 invoking:1 bahmani:1 minmax:1 contains:1 liu:1 selecting:2 offering:1 interestingly:1 undiscovered:2 existing:4 current:2 com:1 ust:1 additive:2 enables:1 update:1 v:1 gr... |
4,742 | 5,294 | A Safe Screening Rule for Sparse Logistic Regression
Jiayu Zhou
Arizona State University
Tempe, AZ 85287
jiayu.zhou@asu.edu
Jie Wang
Arizona State University
Tempe, AZ 85287
jie.wang.ustc@asu.edu
Jun Liu
SAS Institute Inc.
Cary, NC 27513
jun.liu@sas.com
Peter Wonka
Arizona State University
Tempe, AZ 85287
peter.wonk... | 5294 |@word briefly:1 version:1 stronger:1 pillar:1 solver1:1 d2:5 pick:1 mention:2 reduction:1 liu:4 contains:2 score:2 outperforms:3 existing:4 past:1 com:1 yet:1 written:1 import:1 numerical:1 subsequent:1 j1:2 additive:2 remove:2 plot:1 atlas:1 asu:4 ith:2 lr:18 boosting:1 location:1 five:2 rabbani:1 along:3 fittin... |
4,743 | 5,295 | Generalized Dantzig Selector:
Application to the k-support norm
Soumyadeep Chatterjee?
Sheng Chen?
Arindam Banerjee
Dept. of Computer Science & Engg.
University of Minnesota, Twin Cities
{chatter,shengc,banerjee}@cs.umn.edu
Abstract
We propose a Generalized Dantzig Selector (GDS) for linear models, in which any
norm e... | 5295 |@word version:1 norm:80 linearized:3 decomposition:4 tr:8 liu:2 contains:2 series:4 selecting:1 interestingly:2 outperforms:1 existing:3 current:1 ka:1 chu:1 written:1 engg:1 enables:1 designed:1 drop:1 update:13 plot:2 v:4 half:1 fpr:1 lr:5 provides:3 simpler:1 zhang:2 yuan:1 consists:2 prove:1 introduce:1 peng:... |
4,744 | 5,296 | Parallel Feature Selection inspired by Group Testing
Yingbo Zhou?
Utkarsh Porwal?
CSE Department
SUNY at Buffalo
{yingbozh, utkarshp}@buffalo.edu
Ce Zhang
CS Department
University of Wisconsin-Madison
czhang@cs.wisc.edu
Hung Ngo
CSE Department
SUNY at Buffalo
hungngo@buffalo.edu
Christopher R?e
CS Department
Stanford... | 5296 |@word trial:1 madelon:4 middle:2 proportion:1 bekkerman:1 nd:3 open:2 relevancy:1 covariance:3 elisseeff:1 arti:1 thereby:1 wrapper:17 liu:1 series:3 score:30 selecting:1 daniel:1 ours:5 existing:6 si:2 must:2 john:2 subsequent:1 distant:1 designed:2 v:1 intelligence:2 selected:8 devising:1 item:4 accordingly:1 c... |
4,745 | 5,297 | Spectral k-Support Norm Regularization
Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
Department of Computer Science
University College London
{a.mcdonald,m.pontil,d.stamos}@cs.ucl.ac.uk
Abstract
The k-support norm has successfully been applied to sparse vector prediction
problems. We observe that it belongs... | 5297 |@word multitask:5 trial:1 briefly:1 norm:185 jacob:2 mention:1 tr:21 substitution:1 contains:1 series:1 recovered:2 wd:3 od:1 comparing:2 mahoudeaux:1 toh:1 olkin:1 written:3 readily:1 bd:1 must:1 numerical:4 rudin:1 provides:2 characterization:1 org:1 u2i:1 direct:1 manner:1 introduce:3 expected:1 rapid:1 multi:... |
4,746 | 5,298 | Beta-Negative Binomial Process and Exchangeable
Random Partitions for Mixed-Membership Modeling
Mingyuan Zhou
IROM Department, McCombs School of Business
The University of Texas at Austin, Austin, TX 78712, USA
mingyuan.zhou@mccombs.utexas.edu
Abstract
The beta-negative binomial process (BNBP), an integer-valued stoc... | 5298 |@word m1j:2 middle:2 proportion:2 njk:25 selecting:1 document:11 past:1 existing:2 outperforms:1 current:1 yet:1 assigning:1 fn:1 additive:2 partition:38 remove:1 plot:7 update:3 mackey:1 intelligence:2 instantiate:1 nq:1 blei:6 provides:3 evy:1 traverse:2 five:2 unbounded:2 favaro:1 direct:4 beta:21 become:1 vjk... |
4,747 | 5,299 | The Infinite Mixture of Infinite Gaussian Mixtures
Bartek Rajwa
Bindley Bioscience Center
Purdue University
W. Lafayette, IN 47907
rajwa@cyto.purdue.edu
Halid Z. Yerebakan
Department of
Computer and Information Science
IUPUI
Indianapolis, IN 46202
hzyereba@cs.iupui.edu
Murat Dundar
Department of
Computer and Informat... | 5299 |@word repository:2 version:9 proportion:1 hu:1 tamayo:1 covariance:11 accommodate:1 contains:3 score:12 lichman:1 ecole:1 document:2 existing:3 current:2 comparing:1 com:2 scatter:3 readily:1 visible:1 shape:3 enables:1 eleven:1 plot:1 sponsored:1 update:1 generative:5 igmm:1 leaf:4 half:1 xk:1 core:1 blei:2 iter... |
4,748 | 53 | 338
The Connectivity Analysis of Simple Association
- orHow Many Connections Do You Need!
Dan Hammerstrom *
Oregon Graduate Center, Beaverton, OR 97006
ABSTRACT
The efficient realization, using current silicon technology, of Very Large Connection
Networks (VLCN) with more than a billion connections requires that these... | 53 |@word version:1 compression:25 proportion:1 simulation:4 simplifying:1 reduction:1 itp:1 precluding:1 past:1 existing:2 current:3 nt:1 yet:1 conjunctive:1 must:5 written:1 john:1 distant:1 analytic:2 v:1 selected:2 fewer:1 accordingly:2 plane:1 dissertation:1 underestimating:1 provides:2 equi:1 node:13 unbounded:1 ... |
4,749 | 530 | The Effective Number of Parameters:
An Analysis of Generalization and Regularization
in Nonlinear Learning Systems
John E. Moody
Department of Computer Science, Yale University
P.O. Box 2158 Yale Station, New Haven, CT 06520-2158
Internet: moody@cs.yale.edu, Phone: (203)432-1200
Abstract
We present an analysis of how... | 530 |@word version:1 nd:1 dekker:2 confirms:1 linearized:4 necessity:1 series:1 contains:1 denoting:1 current:1 comparing:1 surprising:1 trc:1 dx:1 must:6 perturbative:1 john:1 additive:1 shape:1 remove:1 msb:3 stationary:1 math:1 location:1 preference:1 five:1 ect:1 incorrect:1 consists:2 symp:1 huber:3 expected:16 ol... |
4,750 | 5,300 | Capturing Semantically Meaningful Word
Dependencies with an Admixture of Poisson MRFs
David I. Inouye
Pradeep Ravikumar
Inderjit S. Dhillon
Department of Computer Science
University of Texas at Austin
{dinouye,pradeepr,inderjit}@cs.utexas.edu
Abstract
We develop a fast algorithm for the Admixture of Poisson MRFs (AP... | 5300 |@word aircraft:2 seems:2 plsa:3 open:3 seek:3 covariance:3 hsieh:1 tr:1 initial:3 liu:2 uncovered:1 score:23 selecting:4 united:1 document:14 outperforms:2 current:3 com:1 yet:3 must:4 written:1 realistic:1 partition:2 hofmann:1 interpretable:1 fund:1 update:1 intelligence:1 selected:3 fewer:1 leaf:1 parameteriza... |
4,751 | 5,301 | Dynamic Rank Factor Model for Text Streams
Shaobo Han?, Lin Du?, Esther Salazar and Lawrence Carin
Duke University, Durham, NC 27708
{shaobo.han, lin.du, esther.salazar, lcarin}@duke.edu
Abstract
We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence... | 5301 |@word cu:1 inversion:2 proportion:2 loading:2 nd:2 villani:1 cco:1 simulation:1 r:1 covariance:6 decomposition:3 pressure:1 gamerman:1 tr:1 ld:1 liu:1 series:9 score:4 united:6 unx:1 t7:6 offering:1 contains:4 pub:2 genetic:1 document:27 longitudinal:1 reaction:1 current:1 wd:2 virus:3 nt:28 si:1 readily:1 numeri... |
4,752 | 5,302 | A provable SVD-based algorithm for learning topics
in dominant admixture corpus
Trapit Bansal?, C. Bhattacharyya??
Department of Computer Science and Automation
Indian Institute of Science
Bangalore -560012, India
?trapitbansal@gmail.com
?chiru@csa.iisc.ernet.in
Ravindran Kannan
Microsoft Research
India
kannan@micros... | 5302 |@word trial:6 version:2 polynomial:3 seems:1 norm:2 decomposition:1 p0:8 sheffet:1 pick:6 solid:1 liu:1 score:1 document:89 bhattacharyya:1 outperforms:2 existing:2 com:3 gmail:1 yet:1 assigning:1 realistic:3 numerical:1 subsequent:1 partition:6 seeding:1 plot:1 succeeding:1 fewer:1 selected:1 xk:1 mccallum:1 ble... |
4,753 | 5,303 | Learning a Concept Hierarchy from
Multi-labeled Documents
1
Viet-An Nguyen1?, Jordan Boyd-Graber2 , Philip Resnik1,3,4 , Jonathan Chang5
2
5
Computer Science, 3 Linguistics, 4 UMIACS
Computer Science
Facebook
Univ. of Maryland, College Park, MD
Univ. of Colorado, Boulder, CO Menlo Park, CA
vietan@cs.umd.edu
Jordan.Bo... | 5303 |@word aircraft:1 version:1 middle:1 bigram:3 proportion:1 nd:5 open:1 hu:1 shot:1 ld:13 reduction:1 initial:1 liu:3 contains:1 score:2 document:63 outperforms:2 existing:1 africa:1 current:1 com:1 wd:16 assigning:1 chu:1 stemming:1 kdd:1 designed:1 interpretable:6 update:2 kristina:1 generative:2 discovering:1 le... |
4,754 | 5,304 | On a Theory of Nonparametric Pairwise Similarity
for Clustering: Connecting Clustering to
Classification
Yingzhen Yang1 Feng Liang1 Shuicheng Yan2 Zhangyang Wang1 Thomas S. Huang1
1
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
{yyang58,liangf,zwang119,t-huang1}@illinois.edu
2
National University of... | 5304 |@word mild:2 repository:1 version:1 c0:2 open:1 shuicheng:1 configuration:1 series:1 existing:1 dx:2 john:1 deniz:1 ronald:1 fn:1 partition:6 shape:1 analytic:1 n0:7 isotropic:1 yamada:1 record:1 node:1 constructed:1 prove:3 weinstein:1 assaf:1 introduce:3 manner:1 pairwise:45 torbj:1 frequently:1 multi:4 inspire... |
4,755 | 5,305 | Robust Bayesian Max-Margin Clustering
Changyou Chen?
Jun Zhu?
Xinhua Zhang]
?
Dept. of Electrical and Computer Engineering, Duke University, Durham, NC, USA
?
State Key Lab of Intelligent Technology & Systems; Tsinghua National TNList Lab;
?
Dept. of Computer Science & Tech., Tsinghua University, Beijing 100084, China... | 5305 |@word mild:1 repository:2 briefly:1 changyou:1 proportion:1 seems:1 reused:1 open:1 calculus:2 hu:1 thereby:2 accommodate:1 tnlist:1 contains:1 score:5 selecting:1 lichman:1 initialisation:1 document:22 outperforms:3 existing:6 com:1 si:10 gmail:1 determinantal:1 subsequent:1 partition:1 informative:1 enables:1 a... |
4,756 | 5,306 | On Integrated Clustering and Outlier Detection
Linsey Pang
University of Sydney
qlinsey@it.usyd.edu.au
Lionel Ott
University of Sydney
lott4241@uni.sydney.edu.au
Sanjay Chawla
University of Sydney
sanjay.chawla@sydney.edu.au
Fabio Ramos
University of Sydney
fabio.ramos@sydney.edu.au
Abstract
We model the joint clu... | 5306 |@word determinant:4 version:1 norm:2 covariance:4 pick:1 ronchetti:1 initial:4 contains:2 score:3 selecting:4 document:1 outperforms:1 comparing:1 si:2 assigning:1 must:1 john:1 belmont:1 shape:1 remove:2 plot:1 interpretable:2 update:2 seeding:1 half:1 selected:12 fewer:1 xk:3 underestimating:1 lr:36 completenes... |
4,757 | 5,307 | Convex Optimization Procedure for Clustering:
Theoretical Revisit
Changbo Zhu
Department of Electrical and Computer Engineering
Department of Mathematics
National University of Singapore
elezhuc@nus.edu.sg
Chenlei Leng
Department of Statistics
University of Warwick
c.leng@warwick.ac.uk
Huan Xu
Department of Mechanical... | 5307 |@word armand:1 briefly:1 norm:10 d2:8 shuicheng:1 seek:1 simulation:1 km:3 heiser:1 solid:1 mpexuh:1 liu:1 series:1 outperforms:1 ksk1:1 ka:3 recovered:1 adj:1 si:1 yet:1 written:1 john:1 numerical:3 concatenate:3 thrust:1 shape:1 pelckmans:2 xk:1 ith:4 provides:2 theodoros:1 c2:10 constructed:5 ik:2 n22:2 introd... |
4,758 | 5,308 | Greedy Subspace Clustering
Dohyung Park
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
dhpark@utexas.edu
Constantine Caramanis
Dept. of Electrical and Computer Engineering
The University of Texas at Austin
constantine@utexas.edu
Sujay Sanghavi
Dept. of Electrical and Computer Engineeri... | 5308 |@word trial:1 polynomial:3 norm:7 proportion:2 nd:1 unif:1 d2:1 pick:2 liu:2 contains:4 born:1 zimek:1 ours:1 existing:8 bradley:1 current:1 recovered:1 com:1 numerical:1 greedy:11 selected:2 intelligence:4 desktop:1 plane:1 gpca:2 math:1 simpler:2 zhang:1 five:2 mathematical:1 constructed:3 direct:1 c2:6 become:... |
4,759 | 5,309 | Graph Clustering With Missing Data : Convex
Algorithms and Analysis
Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
Department of Electrical Engineering
California Institute of Technology, Pasadena, CA 91125
{ramya, soymak}@caltech.edu, hassibi@systems.caltech.edu
Abstract
We consider the problem of finding cluste... | 5309 |@word faculty:1 briefly:1 norm:6 tedious:1 proportionality:2 simulation:5 condon:1 decomposition:3 q1:1 minming:1 solid:4 klk:2 mishra:1 ksk1:2 current:1 blank:1 recovered:8 comparing:3 com:1 si:3 anne:1 yet:1 must:1 written:1 john:3 numerical:1 partition:2 kdd:2 visibility:1 plot:3 succeeding:1 santo:1 provides:... |
4,760 | 531 | Node Splitting: A Constructive Algorithm for
Feed-Forward Neural Networks
Mike Wynne-Jones
Research Initiative in Pattern Recognition
St. Andrews Road, Great Malvern
WR14 3PS, UK
mikewj@hermes.mod.uk
Abstract
A constructive algorithm is proposed for feed-forward neural networks,
which uses node-splitting in the hidde... | 531 |@word cox:1 proportion:3 ivit:2 disk:1 decomposition:2 covariance:1 thereby:1 mention:1 reduction:1 initial:4 selecting:1 interestingly:1 current:3 comparing:1 scatter:5 import:1 creat:1 john:4 numerical:1 wynne:6 plot:4 update:11 recept:2 half:1 selected:1 plane:1 provides:1 node:73 ron:2 contribute:1 direct:3 in... |
4,761 | 5,310 | Dimensionality Reduction with Subspace Structure
Preservation
Ifeoma Nwogu
Department of Computer Science
SUNY Buffalo
Buffalo, NY 14260
inwogu@buffalo.edu
Devansh Arpit
Department of Computer Science
SUNY Buffalo
Buffalo, NY 14260
devansha@buffalo.edu
Venu Govindaraju
Department of Computer Science
SUNY Buffalo
Buff... | 5310 |@word compression:3 norm:4 nd:1 hu:1 d2:4 shuicheng:1 tried:1 covariance:1 lpp:2 reduction:21 liu:1 ours:5 outperforms:1 existing:1 si:5 assigning:1 must:2 john:3 concatenate:1 rd2:2 intelligence:2 plane:7 xk:7 ith:10 gpca:1 zhang:1 along:7 kvk2:1 sii:6 qualitative:2 prove:1 consists:3 x0:1 theoretically:3 inter:... |
4,762 | 5,311 | Distributed Balanced Clustering via Mapping
Coresets
Aditya Bhaskara
Google NYC
bhaskaraaditya@google.com
MohammadHossein Bateni
Google NYC
bateni@google.com
Vahab Mirrokni
Google NYC
mirrokni@google.com
Silvio Lattanzi
Google NYC
silviol@google.com
Abstract
Large-scale clustering of data points in metric spaces is... | 5311 |@word mild:1 version:2 seems:1 open:3 bicriteria:6 harder:1 bahmani:2 reduction:2 initial:1 contains:1 silviol:1 com:4 si:1 attracted:1 written:1 partition:7 kdd:1 alone:1 greedy:1 selected:3 pvldb:1 short:1 core:1 multiset:4 bijection:6 location:5 node:3 math:1 simpler:2 c2:1 constructed:2 symposium:3 focs:1 pro... |
4,763 | 5,312 | Zeta Hull Pursuits:
Learning Nonconvex Data Hulls
?
Yuanjun Xiong? Wei Liu? Deli Zhao Xiaoou Tang?
Information Engineering Department, The Chinese University of Hong Kong, Hong Kong
?
IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
Advanced Algorithm Research Group, HTC, Beijing, China
{yjxiong,... | 5312 |@word kong:3 determinant:1 version:1 inversion:2 polynomial:1 stronger:1 manageable:1 nd:3 seek:1 tried:1 decomposition:8 pick:1 eld:1 thereby:2 nystr:7 reduction:2 liu:5 cyclic:1 score:3 selecting:2 contains:4 tuned:1 existing:3 current:1 com:2 od:1 tackling:1 chu:1 written:2 gpu:1 determinantal:2 stemming:1 joh... |
4,764 | 5,313 | The Bayesian Case Model: A Generative Approach
for Case-Based Reasoning and Prototype
Classification
Been Kim, Cynthia Rudin and Julie Shah
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
{beenkim, rudin, julie a shah}@csail.mit.edu
Abstract
We present the Bayesian Case Model (BCM), a general fram... | 5313 |@word version:1 middle:1 instruction:1 r:1 chili:6 carry:1 zij:12 selecting:1 document:2 interestingly:2 psj:12 subjective:2 freitas:1 current:1 com:1 comparing:1 discretization:1 lang:1 yet:1 must:2 readily:1 additive:1 kdd:1 shape:5 hofmann:1 designed:2 interpretable:6 alone:1 generative:6 rudin:5 discovering:1... |
4,765 | 5,314 | Consistency of Spectral Partitioning of Uniform
Hypergraphs under Planted Partition Model
Debarghya Ghoshdastidar
Ambedkar Dukkipati
Department of Computer Science & Automation
Indian Institute of Science
Bangalore ? 560012, India
{debarghya.g,ad}@csa.iisc.ernet.in
Abstract
Spectral graph partitioning methods have re... | 5314 |@word trial:1 briefly:1 middle:1 version:3 polynomial:1 tensorial:1 km:1 bn:10 decomposition:9 solid:1 liu:1 contains:8 series:1 score:3 nt:1 cad:1 written:1 subsequent:1 partition:34 j1:2 shape:5 plot:3 intelligence:2 ith:3 core:1 provides:6 detecting:6 node:37 characterization:2 mathematical:1 lathauwer:2 const... |
4,766 | 5,315 | Projecting Markov Random Field Parameters for
Fast Mixing
Justin Domke
NICTA, The Australian National University
justin.domke@nicta.com.au
Xianghang Liu
NICTA, The University of New South Wales
xianghang.liu@nicta.com.au
Abstract
Markov chain Monte Carlo (MCMC) algorithms are simple and extremely powerful techniques... | 5315 |@word trial:1 norm:50 heuristically:1 simulation:1 decomposition:2 tr:2 solid:1 liu:3 configuration:2 contains:1 zij:2 com:2 comparing:3 od:1 must:3 john:1 partition:1 update:1 v:1 stationary:6 implying:1 amir:1 parametrization:1 core:1 node:2 gx:1 firstly:1 dn:3 along:1 focs:1 wale:1 downscaled:1 introduce:1 the... |
4,767 | 5,316 | Blossom Tree Graphical Models
John Lafferty
Department of Statistics
Department of Computer Science
University of Chicago
Zhe Liu
Department of Statistics
University of Chicago
Abstract
We combine the ideas behind trees and Gaussian graphical models to form a new
nonparametric family of graphical models. Our approach... | 5316 |@word determinant:1 version:2 polynomial:1 d2:4 simulation:3 bn:1 covariance:7 decomposition:1 solid:2 liu:4 contains:2 score:1 selecting:2 uncovered:1 nonparanormal:18 existing:1 current:1 nicolai:1 negentropy:8 must:2 john:4 chicago:2 partition:4 numerical:1 remove:1 greedy:1 selected:5 signalling:2 fa9550:1 co... |
4,768 | 5,317 | Distributed Parameter Estimation
in Probabilistic Graphical Models
Yariv D. Mizrahi1 Misha Denil2 Nando de Freitas2,3,4
1
University of British Columbia, Canada
2
University of Oxford, United Kingdom
3
Canadian Institute for Advanced Research
4
Google DeepMind
yariv@math.ubc.ca
{misha.denil,nando}@cs.ox.ac.uk
Abstract... | 5317 |@word illustrating:2 cox:1 wiesel:3 stronger:1 confirms:1 simulation:1 decomposition:6 kent:1 contrastive:1 covariance:1 liu:16 contains:6 series:1 united:1 ours:1 freitas:3 bradley:1 import:2 written:3 must:2 ikeda:1 partition:3 enables:2 drop:1 intelligence:5 selected:1 leaf:1 generative:1 inspection:1 xk:1 gey... |
4,769 | 5,318 | Elementary Estimators for Graphical Models
Eunho Yang
IBM T.J. Watson Research Center
eunhyang@us.ibm.com
Aur?elie C. Lozano
IBM T.J. Watson Research Center
aclozano@us.ibm.com
Pradeep Ravikumar
University of Texas at Austin
pradeepr@cs.utexas.edu
Abstract
We propose a class of closed-form estimators for sparsity-st... | 5318 |@word h:2 lognp:1 determinant:2 briefly:1 polynomial:1 norm:8 c0:2 pseudomoment:1 physik:1 simulation:3 seek:1 covariance:15 p0:9 hsieh:1 tr:1 moment:14 initial:1 liu:1 series:1 com:2 surprising:1 luo:1 toh:1 yet:1 attracted:1 written:1 partition:8 pertinent:1 v:2 stationary:1 half:1 instantiate:1 selected:2 para... |
4,770 | 5,319 | Structure learning of
antiferromagnetic Ising models
Guy Bresler1
David Gamarnik2
Devavrat Shah1
Laboratory for Information and Decision Systems
Department of EECS1 and Sloan School of Management2
Massachusetts Institute of Technology
{gbresler,gamarnik,devavrat}@mit.edu
Abstract
In this paper we investigate the compu... | 5319 |@word polynomial:1 achievable:2 stronger:1 d2:2 pg:4 thereby:1 liu:2 contains:2 configuration:3 selecting:1 amp:1 existing:1 comparing:1 repelling:14 yet:1 assigning:1 must:1 partition:3 remove:2 aside:1 greedy:4 fewer:1 intelligence:2 beginning:1 core:11 short:1 detecting:1 node:25 allerton:2 accessed:1 symposiu... |
4,771 | 532 | A Cortico-Cerebellar Model that Learns to
Generate Distributed Motor Commands to
Control a Kinematic Arm
N.E. Berthier S.P. Singh A.G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01002
.T.C. Honk
Department of Physiology
Northwestern University Medical School
Chicago, IL 60611
Abstrac... | 532 |@word trial:2 proportion:1 open:2 termination:2 simulation:8 r:1 tr:1 moment:1 ivaldi:2 initial:5 efficacy:1 selecting:1 current:1 neurophys:1 activation:5 yet:1 reminiscent:1 olive:1 physiol:1 realistic:1 chicago:3 berthier:8 shape:1 motor:35 hypothesize:1 plot:1 half:1 selected:6 nervous:1 plane:1 undertook:1 re... |
4,772 | 5,320 | On Sparse Gaussian Chain Graph Models
Seyoung Kim
Lane Center for Computational Biology
Carnegie Mellon University
sssykim@cs.cmu.edu
Calvin McCarter
Machine Learning Department
Carnegie Mellon University
calvinm@cmu.edu
Abstract
In this paper, we address the problem of learning the structure of Gaussian chain
graph... | 5320 |@word middle:1 integrative:4 pancreatic:2 simulation:9 covariance:6 hsieh:1 jacob:1 tr:1 solid:1 contains:1 series:1 genetic:2 amp:5 cort:1 err:8 current:1 recovered:2 intelligence:4 discovering:1 selected:3 parameterization:2 mccallum:1 lr:25 node:1 zhang:2 five:1 along:3 constructed:3 direct:1 c2:2 qualitative:... |
4,773 | 5,321 | Provable Submodular Minimization using
Wolfe?s Algorithm
Deeparnab Chakrabarty?
Prateek Jain?
Pravesh Kothari?
Abstract
Owing to several applications in large scale learning and vision problems, fast
submodular function minimization (SFM) has become a critical problem. Theoretically, unconstrained SFM can be perfor... | 5321 |@word kohli:1 version:5 polynomial:10 norm:31 seems:1 bf:23 semidifferential:1 termination:3 corral:3 additively:1 interestingly:1 err:28 current:2 si:3 intriguing:1 numerical:1 remove:1 designed:1 drop:2 update:4 plot:1 v:1 implying:2 greedy:3 xk:12 beginning:5 provides:1 completeness:1 node:1 math:3 along:1 con... |
4,774 | 5,322 | A Differential Equation for Modeling Nesterov?s
Accelerated Gradient Method: Theory and Insights
Weijie Su1
2
Stephen Boyd2
Emmanuel J. Cand`es1,3
1
Department of Statistics, Stanford University, Stanford, CA 94305
Department of Electrical Engineering, Stanford University, Stanford, CA 94305
3
Department of Mathema... | 5322 |@word achievable:1 norm:1 tedious:1 open:1 d2:1 simulation:1 pg:5 tr:2 initial:6 series:2 nesta:1 tuned:1 ati:1 kx0:12 nonmonotone:1 comparing:1 dx:1 written:1 numerical:7 analytic:1 update:2 leaf:1 xk:40 vanishing:1 hamiltonian:1 core:1 iterates:1 provides:1 math:1 simpler:2 mathematical:4 along:2 c2:2 branin:1 ... |
4,775 | 5,323 | Learning Distributed Representations for Structured
Output Prediction
Vivek Srikumar?
University of Utah
svivek@cs.utah.edu
Christopher D. Manning
Stanford University
manning@cs.stanford.edu
Abstract
In recent years, distributed representations of inputs have led to performance gains
in many applications by allowing... | 5323 |@word trial:2 version:3 middle:4 norm:4 d2:1 seek:1 decomposition:2 sgd:3 recursively:1 electronics:2 cyclic:1 score:6 document:6 interestingly:1 prefix:1 fa8750:1 outperforms:2 adj:2 lang:1 written:1 parsing:2 shape:1 hofmann:1 intelligence:3 instantiate:1 selected:1 mccallum:1 sys:2 ith:1 preference:1 nnp:1 zha... |
4,776 | 5,324 | Predictive Entropy Search for Efficient Global
Optimization of Black-box Functions
Jos?e Miguel Hern?andez-Lobato
jmh233@cam.ac.uk
University of Cambridge
Matthew W. Hoffman
mwh30@cam.ac.uk
University of Cambridge
Zoubin Ghahramani
zoubin@eng.cam.ac.uk
University of Cambridge
Abstract
We propose a novel information... | 5324 |@word economically:1 middle:1 version:2 briefly:2 inversion:1 advantageous:2 seems:4 repository:1 pcc:1 zilinskas:1 r:7 simulation:2 vanhatalo:1 eng:1 covariance:5 tr:1 reduction:2 initial:1 series:4 lichman:1 selecting:1 past:2 outperforms:2 freitas:4 current:2 discretization:1 riihim:1 dx:1 must:4 written:3 num... |
4,777 | 5,325 | Submodular meets Structured: Finding Diverse
Subsets in Exponentially-Large Structured Item Sets
Adarsh Prasad
UT Austin
adarsh@cs.utexas.edu
Stefanie Jegelka
UC Berkeley
stefje@eecs.berkeley.edu
Dhruv Batra
Virginia Tech
dbatra@vt.edu
Abstract
To cope with the high level of ambiguity faced in domains such as Compu... | 5325 |@word kohli:4 polynomial:2 replicate:1 yi0:1 suitably:1 everingham:1 open:1 seek:3 prasad:1 r:1 rivera:4 reduction:1 initial:1 configuration:9 contains:3 score:5 series:1 siebel:1 liu:2 document:3 batista:1 current:1 contextual:1 yet:2 must:2 reminiscent:1 written:1 determinantal:1 concatenate:1 partition:1 addit... |
4,778 | 5,326 | The Noisy Power Method:
A Meta Algorithm with Applications
Moritz Hardt?
IBM Research Almaden
Eric Price?
IBM Research Almaden
Abstract
We provide a new robust convergence analysis of the well-known power method for
computing the dominant singular vectors of a matrix that we call the noisy power
method. Our result c... | 5326 |@word kgk:1 private:10 polynomial:4 stronger:5 norm:5 achievable:1 open:4 d2:1 crucially:1 covariance:20 decomposition:1 tr:1 harder:1 recursively:1 reduction:1 initial:4 contains:1 daniel:1 past:1 existing:2 com:1 z2:3 surprising:1 jns13:2 must:2 john:1 numerical:6 confirming:1 kdd:1 update:5 fewer:1 item:3 comp... |
4,779 | 5,327 | Two-Layer Feature Reduction for Sparse-Group
Lasso via Decomposition of Convex Sets
Jie Wang, Jieping Ye
Computer Science and Engineering
Arizona State University, Tempe, AZ 85287
{jie.wang.ustc, jieping.ye}@asu.edu
Abstract
Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneou... | 5327 |@word briefly:1 version:1 middle:2 norm:4 stronger:1 grey:2 integrative:1 simulation:1 bn:1 decomposition:7 pg:4 mention:1 reduction:10 liu:3 contains:1 series:4 existing:5 written:2 designed:1 plot:2 half:2 asu:1 discovering:1 selected:2 ith:3 smith:1 provides:2 characterization:2 rabbani:1 mathematical:1 along:... |
4,780 | 5,328 | Median Selection Subset Aggregation for Parallel
Inference
Xiangyu Wang
Dept. of Statistical Science
Duke University
xw56@stat.duke.edu
Peichao Peng
Statistics Department
University of Pennsylvania
ppeichao@yahoo.com
David B. Dunson
Dept. of Statistical Science
Duke University
dunson@stat.duke.edu
Abstract
For massi... | 5328 |@word repository:1 version:1 briefly:1 polynomial:3 proportion:1 c0:18 dekel:1 vldb:1 simulation:3 simplifying:1 reduction:1 liu:1 contains:4 series:1 selecting:4 lichman:1 daniel:1 bradley:1 current:1 com:1 comparing:1 danny:1 chu:1 john:1 numerical:1 partition:7 realistic:1 designed:1 plot:2 bickson:1 aside:1 s... |
4,781 | 5,329 | Asymmetric LSH (ALSH) for Sublinear Time
Maximum Inner Product Search (MIPS)
Ping Li
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
Piscataway, NJ 08854, USA
pingli@stat.rutgers.edu
Anshumali Shrivastava
Department of Computer Science
Computing and Information Science
Corn... | 5329 |@word repository:1 compression:1 norm:15 cs0:13 vldb:1 scg:1 xtest:1 solid:2 score:1 document:1 past:2 existing:4 outperforms:2 current:3 comparing:1 activation:4 dx:1 kdd:2 confirming:1 remove:2 designed:1 plot:2 hash:29 v:1 intelligence:1 item:22 realizing:1 fa9550:1 provides:3 tahoe:1 firstly:1 simpler:1 along... |
4,782 | 533 | Adaptive Elastic Models for Hand-Printed
Character Recognition
Geoffrey E. Hinton, Christopher K. I. Williams and Michael D. Revow
Department of Computer Science, University of Toronto
Toronto, Ontario, Canada M5S lA4
Abstract
Hand-printed digits can be modeled as splines that are governed by about
8 control points .... | 533 |@word deformed:2 proportion:2 underline:1 open:1 grey:1 simulation:2 tried:2 covariance:1 pick:6 harder:1 initial:3 configuration:5 contains:1 united:1 current:2 elliptical:4 yet:1 shape:8 update:3 generative:1 intelligence:1 steepest:1 filtered:1 recompute:2 coarse:1 postal:2 toronto:2 location:20 five:2 height:1... |
4,783 | 5,330 | A Latent Source Model for
Online Collaborative Filtering
Guy Bresler
George H. Chen
Devavrat Shah
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
{gbresler,georgehc,devavrat}@mit.edu
Abstract
Despite the prevalence of collaborative filtering in recom... | 5330 |@word exploitation:3 version:1 polynomial:2 proportion:3 open:1 km:12 seek:2 simulation:1 decomposition:1 asks:2 initial:5 loc:1 score:3 pandora:1 daniel:2 movielens10m:4 ours:1 outperforms:2 existing:3 com:4 yet:5 nt1:1 john:1 v:2 greedy:3 intelligence:1 item:113 prize:4 short:2 smith:1 provides:1 mannor:1 prefe... |
4,784 | 5,331 | Clustering from Labels and Time-Varying Graphs
Shiau Hong Lim
National University of Singapore
mpelsh@nus.edu.sg
Yudong Chen
EECS, University of California, Berkeley
yudong.chen@eecs.berkeley.edu
Huan Xu
National University of Singapore
mpexuh@nus.edu.sg
Abstract
We present a general framework for graph clustering ... | 5331 |@word trial:3 stronger:1 norm:3 c0:1 seek:1 condon:1 decomposition:2 eng:1 pick:1 carry:1 initial:1 mpexuh:1 series:1 contains:1 daniel:1 ours:3 existing:7 recovered:1 subsequent:1 partition:14 kdd:1 remove:1 designed:1 discrimination:2 stationary:2 generative:2 selected:1 accordingly:1 record:2 detecting:1 node:... |
4,785 | 5,332 | Discrete Graph Hashing
?
Wei Liu? Cun Mu? Sanjiv Kumar Shih-Fu Chang?
IBM T. J. Watson Research Center ? Columbia University Google Research
weiliu@us.ibm.com cm3052@columbia.edu
sfchang@ee.columbia.edu sanjivk@google.com
Abstract
Hashing has emerged as a popular technique for fast nearest neighbor search in giga... | 5332 |@word multitask:1 kong:1 kulis:2 version:2 polynomial:1 norm:3 advantageous:1 nd:5 proportion:1 heiser:1 crucially:1 bn:1 simplifying:1 decomposition:1 eld:2 thereby:2 tr:19 accommodate:1 carry:1 initial:6 liu:5 contains:2 series:1 tuned:1 leeuw:1 past:1 existing:4 outperforms:1 com:2 activation:2 tackling:1 sanj... |
4,786 | 5,333 | Large-scale L-BFGS using MapReduce
Weizhu Chen, Zhenghao Wang, Jingren Zhou
Microsoft
{wzchen,zhwang,jrzhou}@microsoft.com
Abstract
L-BFGS has been applied as an effective parameter estimation method for various
machine learning algorithms since 1980s. With an increasing demand to deal
with massive instances and vari... | 5333 |@word multitask:1 illustrating:1 version:1 decomposition:2 citeseer:1 initial:2 born:1 liu:1 series:1 tuned:1 existing:1 current:3 com:1 contextual:1 si:9 yet:2 chu:2 john:1 devin:1 numerical:1 partition:9 enables:3 designed:2 drop:1 update:18 sponsored:1 hash:5 intelligence:1 xk:13 core:6 short:1 node:1 herbrich... |
4,787 | 5,334 | Recovery of Coherent Data via Low-Rank
Dictionary Pursuit
Ping Li
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
Piscataway, NJ 08854, USA
pingli@rutgers.edu
Guangcan Liu
Department of Statistics and Biostatistics
Department of Computer Science
Rutgers University
Piscatawa... | 5334 |@word mild:1 trial:3 version:2 complying:1 mri:1 norm:10 km:6 confirms:1 simulation:1 shuicheng:2 decomposition:3 mention:1 iii1360971:1 solid:1 klk:1 reduction:1 liu:5 contains:1 daniel:1 document:1 interestingly:2 outperforms:2 existing:8 ksk1:3 recovered:2 yet:3 finest:1 john:2 takeo:2 numerical:3 realistic:2 ... |
4,788 | 5,335 | Scalable Methods for Nonnegative Matrix
Factorizations of Near-separable Tall-and-skinny
Matrices
Jason D. Lee
ICME
Stanford University
Stanford, CA
jdl17@stanford.edu
Austin R. Benson
ICME
Stanford University
Stanford, CA
arbenson@stanford.edu
Bartek Rajwa
Bindley Biosciences Center
Purdue University
West Lafeyette, ... | 5335 |@word version:1 middle:2 briefly:1 compression:1 norm:8 disk:4 open:1 simulation:12 decomposition:7 pick:1 reduction:21 liu:1 contains:2 efficacy:2 selecting:2 interestingly:1 outperforms:1 com:2 surprising:1 chu:1 written:1 hou:1 subsequent:2 numerical:1 j1:3 plot:9 update:2 v:2 greedy:3 selected:9 half:1 intell... |
4,789 | 5,336 | Analog Memories in a Balanced Rate-Based
Network of E-I Neurons
Dylan Festa
df325@cam.ac.uk
Guillaume Hennequin
gjeh2@cam.ac.uk
M?at?e Lengyel
m.lengyel@eng.cam.ac.uk
Computational & Biological Learning Lab, Department of Engineering
University of Cambridge, UK
Abstract
The persistent and graded activity often obse... | 5336 |@word trial:23 version:2 dtk:1 norm:3 open:2 heuristically:1 seek:2 pulse:1 eng:1 solid:2 reduction:6 initial:10 contains:1 efficacy:1 selecting:1 tuned:1 interestingly:1 current:2 scatter:1 must:2 subsequent:2 realistic:5 plasticity:3 shape:1 motor:2 plot:1 v:1 implying:2 cue:6 parameterization:1 accordingly:1 b... |
4,790 | 5,337 | Feedforward Learning of Mixture Models
Matthew Lawlor?
Applied Math
Yale University
New Haven, CT 06520
mflawlor@gmail.com
Steven W. Zucker
Computer Science
Yale University
New Haven, CT 06520
zucker@cs.yale.edu
Abstract
We develop a biologically-plausible learning rule that provably converges to the
class means of ... | 5337 |@word version:2 hippocampus:2 stronger:1 proportion:1 km:1 d2:6 seek:1 decomposition:13 moment:1 initial:1 liu:1 fragment:1 daniel:2 ours:1 demarcated:1 o2:1 kmk:1 current:2 com:1 gmail:1 intriguing:1 must:1 written:2 realize:1 numerical:2 j1:2 plasticity:10 m1t:1 enables:1 designed:2 update:32 v:1 tone:1 oldest:... |
4,791 | 5,338 | A Bayesian model for identifying hierarchically
organised states in neural population activity
Patrick Putzky1,2,3 , Florian Franzen1,2,3 , Giacomo Bassetto1,3 , Jakob H. Macke1,3
1
Max Planck Institute for Biological Cybernetics, T?
ubingen
2
Graduate Training Centre of Neuroscience, University of T?
ubingen
3
Bernst... | 5338 |@word neurophysiology:1 trial:12 proportion:1 stronger:1 open:1 integrative:1 simulation:3 pulse:1 accounting:1 jacob:1 series:1 initialisation:1 outperforms:2 current:3 com:1 analysed:2 activation:1 gmail:1 written:1 shape:1 motor:1 plot:1 update:1 generative:1 leaf:2 intelligence:1 shababo:1 anaesthetised:2 sho... |
4,792 | 5,339 | Clustered factor analysis of multineuronal spike data
Lars Buesing1 , Timothy A. Machado1,2 , John P. Cunningham1 and Liam Paninski1
1
Department of Statistics, Center for Theoretical Neuroscience
& Grossman Center for the Statistics of Mind
2
Howard Hughes Medical Institute & Department of Neuroscience
Columbia Unive... | 5339 |@word trial:4 briefly:1 middle:4 interleave:1 loading:20 norm:4 inversion:1 stronger:2 version:1 termination:1 km:2 d2:2 uncovers:1 covariance:3 excited:1 q1:3 tr:2 solid:2 reduction:2 initial:12 series:6 uncovered:1 genetic:3 outperforms:1 existing:1 recovered:1 anne:1 written:1 readily:1 john:3 sergei:1 concate... |
4,793 | 534 | Learning to Make Coherent Predictions in
Domains with Discontinuities
Suzanna Becker and Geoffrey E. Hinton
Department of Computer Science, University of Toronto
Toronto, Ontario, Canada M5S 1A4
Abstract
We have previously described an unsupervised learning procedure that
discovers spatially coherent propertit>_<; of... | 534 |@word version:4 proportion:2 tried:3 jacob:2 pressure:1 recursively:1 initial:1 contains:1 disparity:4 contextual:1 nowlan:3 must:2 slanted:1 john:1 shape:2 treating:1 v:1 discovering:1 plane:1 ith:1 filtered:1 contribute:2 toronto:2 location:13 five:5 fixation:1 fitting:1 introduce:1 expected:2 os:1 themselves:1 ... |
4,794 | 5,340 | Design Principles of the Hippocampal Cognitive Map
Kimberly L. Stachenfeld1 , Matthew M. Botvinick1 , and Samuel J. Gershman2
Princeton Neuroscience Institute and Department of Psychology, Princeton University
2
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
kls4@princeton.edu, matth... | 5340 |@word trial:1 version:1 hippocampus:12 nd:1 open:3 mehta:2 simulation:5 lobe:2 decomposition:9 recapitulate:1 diuk:2 paid:1 harder:1 recursively:4 carry:1 contains:1 interestingly:1 current:5 contextual:4 yet:1 conjunctive:2 must:1 subsequent:2 periodically:1 partition:1 distant:1 shape:2 enables:2 fyhn:2 hypothe... |
4,795 | 5,341 | Scalable Inference for Neuronal Connectivity from
Calcium Imaging
Alyson K. Fletcher
Sundeep Rangan
Abstract
Fluorescent calcium imaging provides a potentially powerful tool for inferring
connectivity in neural circuits with up to thousands of neurons. However, a key
challenge in using calcium imaging for connectivi... | 5341 |@word version:1 heuristically:1 simulation:9 simplifying:1 pick:1 fifteen:1 deisseroth:1 initial:8 amp:11 current:12 discretization:1 si:11 scatter:2 must:2 readily:1 written:1 numerical:1 realistic:2 partition:1 blur:1 enables:1 plot:2 update:21 zik:9 dissertation:1 regressive:1 provides:2 coarse:1 node:10 tahoe... |
4,796 | 5,342 | Sparse space-time deconvolution
for Calcium image analysis
Ferran Diego
Fred A. Hamprecht
Heidelberg Collaboratory for Image Processing (HCI)
Interdisciplinary Center for Scientific Computing (IWR)
University of Heidelberg, Heidelberg 69115, Germany
{ferran.diego,fred.hamprecht}@iwr.uni-heidelberg.de
Abstract
We desc... | 5342 |@word neurophysiology:3 repository:1 middle:3 norm:4 seems:1 disk:1 tedious:1 seek:1 simulation:1 decomposition:3 invoking:1 minus:1 shot:1 schnitzer:1 lepetit:1 carry:1 moment:1 series:6 exclusively:1 denoting:1 deconvolutional:1 subjective:1 existing:2 activation:5 must:1 additive:1 shape:8 motor:2 remove:1 plo... |
4,797 | 5,343 | Spatio-temporal Representations of Uncertainty in
Spiking Neural Networks
Sophie Deneve
Group for Neural Theory, ENS Paris
Rue d?Ulm, 29, Paris, France
sophie.deneve@ens.fr
Cristina Savin
IST Austria
Klosterneuburg, A-3400, Austria
csavin@ist.ac.at
Abstract
It has been long argued that, because of inherent ambiguity... | 5343 |@word mild:2 trial:13 version:4 middle:2 briefly:1 seems:5 open:1 simulation:1 covariance:5 contrastive:1 moment:1 initial:3 cristina:1 tuned:1 ours:1 existing:2 current:3 recovered:1 reminiscent:1 tenet:1 realistic:2 additive:1 plasticity:1 occludes:1 visibility:1 v:3 greedy:1 generative:1 hamiltonian:1 core:2 s... |
4,798 | 5,344 | Conditional Random Field Autoencoders
for Unsupervised Structured Prediction
Waleed Ammar
Chris Dyer
Noah A. Smith
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
{wammar,cdyer,nasmith}@cs.cmu.edu
Abstract
We introduce a framework for unsupervised learning of structured predictors with... | 5344 |@word multitask:1 arabic:1 msr:1 bigram:1 stronger:1 retraining:1 crucially:1 contrastive:4 twolayer:1 tr:1 reduction:1 series:3 score:2 ours:1 outperforms:1 existing:1 must:1 parsing:4 partition:2 enables:1 interpretable:1 sponsored:1 generative:7 selected:1 instantiate:1 mccallum:4 affair:1 smith:5 org:1 tagger... |
4,799 | 5,345 | Learning Generative Models with Visual Attention
Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada
{tang,nitish,rsalakhu}@cs.toronto.edu
Abstract
Attention has long been proposed by psychologists to be important for efficiently
dealing ... | 5345 |@word trial:3 middle:1 briefly:1 nd:1 seek:3 propagate:1 covariance:1 p0:7 simplifying:1 set5:1 contrastive:2 dramatic:1 extrastriate:1 initial:7 configuration:1 foveal:2 generatively:3 contains:2 daniel:1 freitas:1 current:1 activation:1 reminiscent:1 takeo:1 realistic:1 visible:5 informative:1 subsequent:1 part... |
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