Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
2,800 | 3,539 | Fast High-dimensional Kernel Summations Using the
Monte Carlo Multipole Method
Dongryeol Lee
Computational Science and Engineering
Georgia Institute of Technology
Atlanta, GA 30332
dongryel@cc.gatech.edu
Alexander Gray
Computational Science and Engineering
Georgia Institute of Technology
Atlanta, GA 30332
agray@cc.ga... | 3539 |@word version:1 polynomial:1 compression:1 norm:2 decomposition:1 dramatic:1 incurs:1 harder:2 moment:2 reduction:6 liu:1 series:5 score:2 selecting:1 contains:2 initial:1 denoting:1 freitas:1 com:1 lang:1 must:1 john:1 partition:4 enables:1 half:1 leaf:5 intelligence:2 core:1 node:26 five:1 along:1 constructed:3... |
2,801 | 354 | On The Circuit Complexity of Neural Networks
v.
K. Y. Sill
P. Roychowdhury
Information Systems Laboratory
Stanford University
Stanford, CA, 94305
Information Systems Laboratory
Stanford University
Stanford, CA, 94305
A. Orlitsky
AT &T Bell Laboratories
600 Mountain A venue
Murray Hill, NJ, 07974
T. Kailath
Inform... | 354 |@word polynomial:6 nd:2 simplifying:1 decomposition:4 thereby:2 carry:1 com:1 surprising:1 schnitger:1 yet:1 must:1 john:1 fn:1 subsequent:1 hajnal:2 fewer:1 reciprocal:1 compo:2 correlat:1 characterization:1 provides:2 math:1 clarified:1 simpler:2 lor:1 direct:1 prove:5 symp:4 inside:1 introduce:2 themselves:1 ry... |
2,802 | 3,540 | Shape-Based Object Localization
for Descriptive Classification
Geremy Heitz1,?
Gal Elidan2,3,?
Ben Packer2,?
Daphne Koller2
1
Department of Electrical Engineering, Stanford University
2
Department of Computer Science, Stanford University
3
Department of Statistics, Hebrew University, Jerusalem
{gaheitz,bpacker,koller}@... | 3540 |@word middle:2 achievable:2 retraining:1 seek:1 prasad:2 accounting:1 covariance:1 pick:2 tr:1 solid:1 initial:1 configuration:2 contains:1 fragment:1 series:1 fa8750:1 rightmost:1 outperforms:1 existing:1 current:1 ka:8 contextual:1 assigning:1 reminiscent:1 readily:4 subcomponent:1 partition:1 shape:51 v:5 gree... |
2,803 | 3,541 | Deep Learning with Kernel Regularization
for Visual Recognition
Kai Yu
Wei Xu
Yihong Gong
NEC Laboratories America, Cupertino, CA 95014, USA
{kyu, wx, ygong}@sv.nec-labs.com
Abstract
In this paper we aim to train deep neural networks for rapid visual recognition.
The task is highly challenging, largely due to the lac... | 3541 |@word determinant:6 cnn:15 sex:1 seek:1 decomposition:5 pick:2 sgd:12 tr:9 nystr:7 lightweight:1 contains:3 hereafter:1 tuned:1 document:3 outperforms:1 com:1 goldberger:1 must:1 informative:1 wx:1 designed:1 update:4 alone:1 greedy:2 selected:1 generative:1 plane:2 record:1 boosting:1 node:1 zhang:1 constructed:... |
2,804 | 3,542 | Diffeomorphic Dimensionality Reduction
Christian Walder and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics
72076 T?ubingen, Germany
first.last@tuebingen.mpg.de
Abstract
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We arg... | 3542 |@word determinant:1 cox:4 version:2 duda:2 seems:1 nd:1 open:1 km:4 rgb:1 covariance:1 thereby:2 mention:1 reduction:22 celebrated:1 contains:1 efficacy:1 score:1 tuned:1 rkhs:2 document:1 reminiscent:2 must:1 readily:1 cottrell:2 numerical:2 recasting:1 realistic:1 christian:1 pertinent:1 kyb:1 designed:1 plot:1... |
2,805 | 3,543 | Bayesian Network Score Approximation using a
Metagraph Kernel
Benjamin Yackley
Department of Computer Science
University of New Mexico
Eduardo Corona
Courant Institute of Mathematical Sciences
New York University
Terran Lane
Department of Computer Science
University of New Mexico
Abstract
Many interesting problems, ... | 3543 |@word middle:1 polynomial:3 decomposition:1 q1:6 recursively:1 lq2:2 contains:1 score:16 selecting:1 series:1 rkhs:1 current:1 yet:1 must:2 suermondt:1 shape:2 drop:1 alone:1 intelligence:2 plane:2 incredible:1 provides:1 node:26 location:1 contribute:1 mathematical:2 along:1 constructed:1 direct:1 a2j:1 consists... |
2,806 | 3,544 | Inferring rankings under constrained sensing
Srikanth Jagabathula
Devavrat Shah
Laboratory of Information and Decision Systems,
Massachusetts Institute of Technology,
Cambridge, MA 02139.
{jskanth, devavrat}@mit.edu
Abstract
Motivated by applications like elections, web-page ranking, revenue maximization etc., we c... | 3544 |@word kondor:1 version:1 norm:1 p0:1 q1:1 tr:7 necessity:1 series:1 interestingly:2 existing:1 recovered:3 written:2 readily:1 intelligence:1 ith:1 vanishing:1 ck2:1 provides:1 noncommutative:1 revisited:1 node:1 preference:1 constructed:4 direct:1 qij:7 prove:5 doubly:2 naor:1 eleventh:1 indeed:6 p1:19 nor:1 mul... |
2,807 | 3,545 | Policy Search for Motor Primitives in Robotics
Jens Kober, Jan Peters
Max Planck Institute for Biological Cybernetics
Spemannstr. 38
72076 T?bingen, Germany
{jens.kober,jan.peters}@tuebingen.mpg.de
Abstract
Many motor skills in humanoid robotics can be learned using parametrized motor
primitives as done in imitation ... | 3545 |@word trial:3 middle:1 version:3 open:3 simulation:1 moment:1 initial:5 series:2 outperforms:7 freitas:1 current:7 john:1 explorative:2 subsequent:2 additive:2 shape:1 analytic:1 motor:58 plot:1 update:7 v:1 stationary:1 intelligence:4 website:1 imitated:1 steepest:1 preference:1 org:1 rollout:6 become:2 differen... |
2,808 | 3,546 | Nonparametric Bayesian Learning of Switching
Linear Dynamical Systems
Emily B. Fox
Electrical Engineering & Computer Science, Massachusetts Institute of Technology
ebfox@mit.edu
?
Erik B. Sudderth? , Michael I. Jordan??
Electrical Engineering & Computer Science and ? Statistics, University of California, Berkeley
{s... | 3546 |@word trial:2 middle:2 version:1 achievable:1 km:1 simulation:1 simplifying:1 covariance:3 thereby:1 tr:1 pavlovi:1 recursively:3 series:4 denoting:1 ours:1 interestingly:1 existing:1 current:1 recovered:1 yet:2 must:1 additive:1 partition:4 plot:3 n0:2 resampling:2 generative:2 fewer:1 colored:1 blei:1 provides:... |
2,809 | 3,547 | Goal-directed decision making in prefrontal
cortex: A computational framework
Matthew Botvinick
Princeton Neuroscience Institute and
Department of Psychology, Princeton University
Princeton, NJ 08540
matthewb@princeton.edu
James An
Computer Science Department
Princeton University
Princeton, NJ 08540
an@princeton.edu
... | 3547 |@word neurophysiology:1 illustrating:1 hippocampus:1 seems:2 instrumental:2 open:1 integrative:1 simulation:8 rol:1 dramatic:1 recursively:1 reduction:2 initial:4 contains:1 series:1 past:2 current:4 si:2 yet:3 must:1 subsequent:2 motor:1 treating:3 medial:2 generative:1 selected:3 intelligence:3 blei:1 provides:... |
2,810 | 3,548 | Nonlinear causal discovery with additive noise models
Patrik O. Hoyer
University of Helsinki
Finland
Dominik Janzing
MPI for Biological Cybernetics
T?ubingen, Germany
Joris Mooij
MPI for Biological Cybernetics
T?ubingen, Germany
Bernhard Sch?olkopf
MPI for Biological Cybernetics
T?ubingen, Germany
Jonas Peters
MPI... | 3548 |@word trial:1 illustrating:1 repository:2 proportion:1 nd:1 open:1 hyv:1 simulation:4 seek:1 contains:2 series:1 denoting:2 current:10 comparing:1 must:3 john:1 subsequent:1 additive:10 shape:1 remove:1 plot:1 fund:1 implying:1 generative:1 intelligence:2 math:2 node:1 herbrich:1 org:1 zhang:1 five:1 bowman:1 dif... |
2,811 | 3,549 | Fast Prediction on a Tree
Mark Herbster, Massimiliano Pontil, Sergio Rojas-Galeano
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, England, UK
{m.herbster, m.pontil,s.rojas}@cs.ucl.ac.uk
Abstract
Given an n-vertex weighted tree with structural diameter S and a subset of m vertic... | 3549 |@word trial:1 middle:2 briefly:1 norm:7 seems:1 vi1:1 nd:2 galeano:1 tr:4 solid:1 nystr:2 initial:1 selecting:1 existing:1 current:2 must:1 mst:21 partition:1 enables:1 plot:2 v:2 greedy:1 leaf:2 prohibitive:1 intelligence:1 betweenness:1 core:1 num:2 rntot:1 detecting:1 provides:2 node:1 balc:1 davison:1 math:2 ... |
2,812 | 355 | A Neural Expert System with Automated Extraction
of Fuzzy If-Then Rules and Its Application to
Medical Diagnosis
Yoichi Hayashi*
Department of Computer and Information Sciences
Ibaraki University
Hitachi-shi,Ibaraki 316, Japan
ABSTRACT
This paper proposes ajuzzy neural expert system (FNES) with the
following two functi... | 355 |@word nd:1 sex:1 termination:3 llo:1 tr:1 reduction:1 configuration:1 pub:1 subjective:2 activation:7 xlr:1 yet:3 intelligence:1 selected:8 item:3 shj:12 thermometer:1 diagnosing:5 lor:4 direct:1 mgt:1 symposium:1 qualitative:1 prove:3 consists:5 manner:3 inter:1 roughly:1 automatically:3 provided:2 kind:1 substan... |
2,813 | 3,550 | Domain Adaptation with Multiple Sources
Yishay Mansour
Google Research and
Tel Aviv Univ.
Mehryar Mohri
Courant Institute and
Google Research
Afshin Rostamizadeh
Courant Institute
New York University
mansour@tau.ac.il
mohri@cims.nyu.edu
rostami@cs.nyu.edu
Abstract
This paper presents a theoretical analysis of th... | 3550 |@word seems:1 disk:1 hu:8 seek:1 pratim:1 blender:1 jacob:1 pick:1 reduction:1 electronics:5 contains:1 att:1 outperforms:2 discretization:1 com:2 gauvain:1 must:1 parsing:1 john:4 plot:3 prohibitive:1 xk:1 ith:1 pointer:1 provides:1 draft:1 five:1 consists:7 prove:2 interscience:1 upenn:1 expected:14 examine:1 l... |
2,814 | 3,551 | Unlabeled data: Now it helps, now it doesn?t
Aarti Singh, Robert D. Nowak?
Department of Electrical and Computer Engineering
University of Wisconsin - Madison
Madison, WI 53706
{singh@cae,nowak@engr}.wisc.edu
Xiaojin Zhu?
Department of Computer Sciences
University of Wisconsin - Madison
Madison, WI 53706
jerryzhu@cs.... | 3551 |@word kgk:1 version:1 polynomial:5 proportion:1 norm:3 logmm:4 nd:5 tr:2 series:1 fragment:3 outperforms:1 z2:2 fn:5 chicago:1 partition:2 ainen:1 v:1 implying:1 alone:1 fewer:1 intelligence:1 xk:1 characterization:4 provides:3 mhm:1 nussbaum:1 along:2 c2:3 clairvoyant:8 hellinger:1 pairwise:1 theoretically:2 exp... |
2,815 | 3,552 | Modeling Short-term Noise Dependence
of Spike Counts in Macaque Prefrontal Cortex
Arno Onken
Technische Universit?at Berlin
/ BCCN Berlin
aonken@cs.tu-berlin.de
?
Steffen Grunew?
alder
Technische Universit?at Berlin
Franklinstr. 28/29, 10587 Berlin, Germany
gruenew@cs.tu-berlin.de
Matthias Munk
MPI for Biological Cy... | 3552 |@word trial:4 nd:2 d2:2 covariance:4 jacob:1 thereby:5 carry:3 initial:1 elliptical:2 discretization:1 yet:2 must:1 bd:1 written:1 subsequent:2 j1:4 shape:5 fx1:2 selected:2 theoretician:1 ith:1 smith:1 short:5 filtered:1 provides:5 tolhurst:1 revisited:1 successive:1 mathematical:1 become:1 differential:1 fittin... |
2,816 | 3,553 | Sparse Convolved Gaussian Processes for
Multi-output Regression
Neil D. Lawrence
School of Computer Science
University of Manchester, U.K.
neill@cs.man.ac.uk
Mauricio Alvarez
School of Computer Science
University of Manchester, U.K.
alvarezm@cs.man.ac.uk
Abstract
We present a sparse approximation approach for depend... | 3553 |@word cu:3 briefly:1 inversion:1 covariance:35 nystr:1 solid:2 igp:3 reduction:2 initial:1 lqr:2 recovered:1 current:1 surprising:1 must:1 written:2 multioutput:1 informative:2 s21:1 intelligence:1 prohibitive:1 selected:1 lr:2 location:10 herbrich:1 org:1 height:7 along:1 differential:2 consists:2 overhead:3 int... |
2,817 | 3,554 | Exact Convex Confidence-Weighted Learning
Koby Crammer Mark Dredze Fernando Pereira?
Department of Computer and Information Science , University of Pennsylvania
Philadelphia, PA 19104
{crammer,mdredze,pereira}@cis.upenn.edu
Abstract
Confidence-weighted (CW) learning [6], an online learning method for linear classifier... | 3554 |@word version:6 stronger:1 norm:4 nd:2 dekel:1 simulation:1 covariance:17 simplifying:1 tr:5 solid:1 initial:1 tuned:1 outperforms:1 current:4 yet:1 assigning:1 written:2 must:2 additive:1 informative:1 plot:1 update:27 intelligence:2 fewer:1 warmuth:3 ith:1 provides:1 herbrich:2 five:1 mathematical:2 c2:1 sympos... |
2,818 | 3,555 | Artificial Olfactory Brain for Mixture Identification
Mehmet K. Muezzinoglu1
Nikolai F. Rulkov1
Alexander Vergara1
Heny D. I. Abarbanel1
1
Institute for Nonlinear Science
University of California San Diego
9500 Gilman Dr., La Jolla, CA, 92093-0402
Ramon Huerta1
Allen Selverston1
2
Thomas Nowotny2
Mikhail I. Rab... | 3555 |@word mild:1 schmuker:1 version:1 seems:1 simulation:3 lobe:17 accommodate:2 reduction:1 initial:2 series:8 contains:2 envision:1 current:1 activation:1 mushroom:7 scatter:2 must:2 yet:1 dx:1 subsequent:4 realistic:1 additive:1 plasticity:6 visible:1 enables:1 remove:1 reproducible:1 discrimination:3 alone:1 gene... |
2,819 | 3,556 | Kernel Change-point Analysis
Za??d Harchaoui
LTCI, TELECOM ParisTech and CNRS
46, rue Barrault, 75634 Paris cedex 13, France
zaid.harchaoui@enst.fr
Francis Bach
Willow Project, INRIA-ENS
45, rue d?Ulm, 75230 Paris, France
francis.bach@mines.org
?
Eric
Moulines
LTCI, TELECOM ParisTech and CNRS
46, rue Barrault, 75634... | 3556 |@word trial:1 version:2 briefly:1 inversion:1 proportion:1 nd:1 open:1 d2:7 simulation:1 bn:11 covariance:10 invoking:1 minus:1 tr:2 series:2 exclusively:1 contains:2 rkhs:3 scovel:1 comparing:1 nt:1 ida:1 yet:1 readily:1 realize:1 partition:6 zaid:1 hoping:1 resampling:2 xk:7 isotropic:1 mccallum:1 recherche:1 m... |
2,820 | 3,557 | Multi-resolution Exploration in Continuous Spaces
Ali Nouri
Department of Computer Science
Rutgers University
Piscataway , NJ 08854
nouri@cs.rutgers.edu
Michael L. Littman
Department of Computer Science
Rutgers University
Piscataway , NJ 08854
mlittman@cs.rutgers.edu
Abstract
The essence of exploration is acting to ... | 3557 |@word h:1 mild:1 trial:1 version:11 middle:1 polynomial:4 norm:1 simulation:1 versatile:1 configuration:2 contains:1 selecting:1 tuned:1 bc:1 current:1 discretization:12 comparing:1 si:1 yet:1 must:2 written:1 refines:2 periodically:1 partition:2 realistic:1 shape:1 hypothesize:2 v:1 half:4 fewer:2 leaf:3 greedy:... |
2,821 | 3,558 | Bayesian Experimental Design of Magnetic
Resonance Imaging Sequences
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38
72012 T?ubingen, Germany
{seeger,hn,rolf.pohmann,bs}@tuebingen.mpg.de
Abstract
We show how improved sequences ... | 3558 |@word trial:1 mri:14 interleave:3 seems:5 stronger:1 pulse:4 tried:2 covariance:2 p0:2 decomposition:1 tr:1 solid:1 shot:2 initial:1 contains:1 score:7 ours:3 current:3 recovered:1 comparing:2 transferability:1 yet:1 readily:2 john:1 numerical:3 realistic:3 subsequent:3 shape:1 cheap:1 designed:1 update:4 fewer:2... |
2,822 | 3,559 | Logistic Normal Priors for Unsupervised
Probabilistic Grammar Induction
Shay B. Cohen Kevin Gimpel Noah A. Smith
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
{scohen,kgimpel,nasmith}@cs.cmu.edu
Abstract
We explore a new Bayesian model for probabilistic grammars, a family of
dis... | 3559 |@word faculty:1 version:1 seek:1 tried:1 covariance:7 recursively:2 initial:1 contains:1 document:4 interestingly:1 past:1 outperforms:1 parsing:8 john:1 update:3 alone:1 generative:3 fewer:1 ith:2 smith:2 short:1 blei:5 coarse:1 nnp:3 competitiveness:1 shorthand:1 combine:1 inside:3 x0:4 tagging:3 expected:4 beh... |
2,823 | 356 | A Theory for Neural Networks with Time Delays
Jose C. Principe
Department of Electrical Engineering
University of Horida, CSE 444
Gainesville, FL 32611
Bert de Vries
Department of Electrical Engineering
University of Horida, CSE 447
Gainesville, FL 32611
Abstract
We present a new neural network model for processing ... | 356 |@word effect:1 implemented:1 normalized:3 involves:1 establish:1 polynomial:1 question:2 occurs:1 concentration:1 illustrated:1 gainesville:2 diagonal:1 self:1 gradient:2 implementing:1 mx:1 link:1 criterion:1 substitution:1 contains:1 generalization:1 decompose:1 generalized:1 w0:1 mathematically:1 past:3 existin... |
2,824 | 3,560 | Online Models for Content Optimization
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Nitin Motgi, Seung-Taek Park,
Raghu Ramakrishnan, Scott Roy, Joe Zachariah
Yahoo! Inc.
701 First Avenue
Sunnyvale, CA 94089
Abstract
We describe a new content publishing system that selects articles to serve to a user,
choosing fr... | 3560 |@word trial:2 eliminating:1 stronger:1 open:1 seek:1 tried:1 simulation:1 r:2 tr:1 harder:1 initial:1 series:4 score:8 selecting:3 past:2 reaction:1 existing:1 current:6 comparing:2 nt:4 com:1 must:4 periodically:3 subsequent:1 additive:1 kdd:2 remove:1 update:4 v:2 stationary:3 pursued:1 selected:3 precaution:1 ... |
2,825 | 3,561 | Non-parametric Regression Between Manifolds
1
Florian Steinke1 , Matthias Hein2
Max Planck Institute for Biological Cybernetics, 72076 T?ubingen, Germany
2
Saarland University, 66041 Saarbr?ucken, Germany
steinke@tuebingen.mpg.de, hein@cs.uni-sb.de
Abstract
This paper discusses non-parametric regression between Riem... | 3561 |@word briefly:1 polynomial:8 norm:2 tensorial:1 open:2 r:10 p0:2 thereby:1 initial:3 contains:1 score:1 outperforms:2 jupp:1 discretization:1 yet:2 dx:8 written:1 additive:1 shape:9 analytic:1 depict:1 prohibitive:1 selected:1 plane:2 inspection:1 parametrization:2 iso:3 vanishing:1 coarse:1 simpler:1 saarland:1 ... |
2,826 | 3,562 | Regularized Co-Clustering with Dual Supervision
Vikas Sindhwani
Jianying Hu
Aleksandra Mojsilovic
IBM Research, Yorktown Heights, NY 10598
{vsindhw, jyhu, aleksand}@us.ibm.com
Abstract
By attempting to simultaneously partition both the rows (examples) and columns
(features) of a data matrix, Co-clustering algorithms ... | 3562 |@word version:2 briefly:1 inversion:1 middle:1 norm:2 hu:1 rgb:1 decomposition:1 tr:10 reduction:1 score:5 selecting:2 tuned:2 rkhs:5 document:12 outperforms:2 existing:1 com:1 wd:1 comparing:1 partition:7 kdd:3 plot:1 update:2 stationary:1 half:1 prohibitive:1 yr:10 intelligence:1 plane:1 mccallum:1 ith:2 node:2... |
2,827 | 3,563 | Psychiatry: insights into depression through
normative decision-making models
Quentin JM Huys1,2,? Joshua T Vogelstein3,? and Peter Dayan2,?
Center for Theoretical Neuroscience, Columbia University, New York, NY 10032, USA
2
Gatsby Computational Neuroscience Unit, University College London, London, WC1N 3AR, UK
3
John... | 3563 |@word mild:1 trial:3 exploitation:1 briefly:1 version:3 instrumental:1 approved:1 stronger:1 replicate:1 sex:1 instruction:1 confirms:1 simulation:1 tried:2 paid:1 thereby:1 tr:2 initial:1 responsivity:1 score:7 pub:1 ours:1 longitudinal:2 err:1 current:4 comparing:2 nt:15 yet:3 john:2 subsequent:2 subcomponent:1... |
2,828 | 3,564 | Correlated Bigram LSA for Unsupervised Language
Model Adaptation
Yik-Cheung Tam?
InterACT, Language Technologies Institute
Carnegie Mellon University
Pittsburgh, PA 15213
yct@cs.cmu.edu
Tanja Schultz
InterACT, Language Technologies Institute
Carnegie Mellon University
Pittsburgh, PA 15213
tanja@cs.cmu.edu
Abstract
W... | 3564 |@word arabic:4 middle:1 briefly:1 yct:1 bigram:78 seems:1 nd:2 disk:1 c0:1 propagate:1 bn:2 decomposition:1 reduction:6 initial:5 tuned:1 document:21 bootstrapped:1 bc:2 current:2 contextual:1 z2:1 written:1 must:1 speakerindependent:1 enables:1 leaf:2 mccallum:1 blei:2 rescoring:3 node:10 firstly:3 window:1 cach... |
2,829 | 3,565 | Learning a Discriminative Hidden Part Model for
Human Action Recognition
Yang Wang
School of Computing Science
Simon Fraser University
Burnaby, BC, Canada, V5A 1S6
ywang12@cs.sfu.ca
Greg Mori
School of Computing Science
Simon Fraser University
Burnaby, BC, Canada, V5A 1S6
mori@cs.sfu.ca
Abstract
We present a discrim... | 3565 |@word version:2 dalal:1 seems:2 nd:6 triggs:1 cla:2 carry:1 shechtman:1 contains:5 bc:2 outperforms:3 blank:2 yet:1 cottrell:1 informative:2 shape:4 v:1 alone:2 half:2 selected:1 discovering:1 parameterization:1 xk:1 mccallum:1 colored:1 location:4 firstly:1 five:1 direct:3 become:1 consists:1 ijcv:1 combine:4 pa... |
2,830 | 3,566 | Robust Kernel Principal Component Analysis
Minh Hoai Nguyen & Fernando De la Torre
Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Abstract
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input
space is mapped ... | 3566 |@word version:3 polynomial:3 norm:1 d2:1 seek:2 tr:1 harder:1 initial:1 contains:2 tuned:2 ours:4 ati:1 outperforms:7 existing:5 psarrou:1 ka:1 current:2 realistic:1 additive:3 partition:2 shape:6 enables:1 remove:1 treating:1 update:5 generative:1 selected:1 device:1 ith:3 short:1 record:1 revisited:1 successive... |
2,831 | 3,567 | The Recurrent Temporal Restricted Boltzmann
Machine
Ilya Sutskever, Geoffrey Hinton, and Graham Taylor
University of Toronto
{ilya, hinton, gwtaylor}@cs.utoronto.ca
Abstract
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for
sequences that is able to successfully model (i.e., generate nice-l... | 3567 |@word propagate:1 p0:7 contrastive:5 harder:1 initial:2 past:2 comparing:1 surprising:1 must:1 realistic:2 visible:10 partition:5 update:10 discrimination:1 generative:5 selected:1 intelligence:2 parameterization:1 ith:2 short:2 toronto:1 kvk2:1 persistent:1 qualitative:1 consists:1 shorthand:1 eleventh:1 manner:... |
2,832 | 3,568 | Learning Bounded Treewidth Bayesian Networks
Gal Elidan
Department of Statistics
Hebrew University
Jerusalem, 91905, Israel
galel@huji.ac.il
Stephen Gould
Department of Electrical Engineering
Stanford University
Stanford, CA 94305, USA
sgould@stanford.edu
Abstract
With the increased availability of data for complex ... | 3568 |@word middle:1 briefly:2 polynomial:9 decomposition:8 minus:1 solid:3 harder:1 liu:10 contains:3 score:8 karger:1 ours:7 past:1 current:3 discretization:1 chordal:2 si:2 must:1 readily:1 dechter:1 happen:2 cpds:2 plot:1 update:22 v:3 greedy:10 intelligence:2 accordingly:1 short:1 characterization:3 provides:2 con... |
2,833 | 3,569 | An Online Algorithm for Maximizing
Submodular Functions
Daniel Golovin
Carnegie Mellon University
Pittsburgh, PA 15213
dgolovin@cs.cmu.edu
Matthew Streeter
Google, Inc.
Pittsburgh, PA 15213
mstreeter@google.com
Abstract
We present an algorithm for solving a broad class of online resource allocation
problems. Our onli... | 3569 |@word trial:3 version:4 proportion:1 nd:1 widom:2 prasad:1 incurs:1 accommodate:1 series:1 contains:2 selecting:2 daniel:3 existing:1 com:1 assigning:1 dx:2 must:6 written:1 subsequent:1 periodically:2 designed:1 ligett:1 bart:1 greedy:18 selected:1 guess:1 intelligence:1 warmuth:1 ith:2 smith:1 record:4 manfred:... |
2,834 | 357 | Learning Theory and Experiments with
Competitive Networks
Griff L. Bilbro
North Carolina State University
Box 7914
Raleigh, NC 27695-7914
David E. Van den Bout
North Carolina State University
Box 7914
Raleigh, NC 27695-7914
Abstract
We apply the theory of Tishby, Levin, and Sol1a (TLS) to two problems.
First we anal... | 357 |@word trial:2 carolina:2 tr:1 moment:3 initial:1 configuration:1 contains:1 tabulate:1 dzp:1 jaynes:2 assigning:1 bd:1 additive:2 numerical:2 realistic:2 analytic:1 motor:1 remove:1 asymptote:1 plot:1 fewer:1 selected:1 conscience:3 along:1 constructed:2 become:1 theoretically:1 overline:1 expected:3 behavior:2 ex... |
2,835 | 3,570 | Short-Term Depression in VLSI Stochastic Synapse
Peng Xu, Timothy K. Horiuchi, and Pamela Abshire
Department of Electrical and Computer Engineering, Institute for Systems Research
University of Maryland, College Park, MD 20742
pxu,timmer,pabshire@umd.edu
Abstract
We report a compact realization of short-term depressi... | 3570 |@word pulse:14 simulation:12 solid:1 reduction:1 liu:1 efficacy:1 tuned:1 existing:2 current:4 attracted:1 written:1 plasticity:13 motor:1 remove:4 designed:1 opin:1 update:1 device:1 short:17 core:1 characterization:1 provides:1 node:3 location:1 five:1 height:1 rc:1 direct:1 m7:2 differential:7 supply:6 vpre:4 ... |
2,836 | 3,571 | Non-stationary dynamic Bayesian networks
Joshua W. Robinson and Alexander J. Hartemink
Department of Computer Science
Duke University
Durham, NC 27708-0129
{josh,amink}@cs.duke.edu
Abstract
A principled mechanism for identifying conditional dependencies in time-series
data is provided through structure learning of dy... | 3571 |@word seems:2 decomposition:1 contraction:1 initial:3 configuration:3 series:18 cyclic:1 hereafter:1 contains:2 seriously:1 existing:1 recovered:2 michal:1 anne:1 si:6 yet:1 must:10 tarantola:1 subsequent:3 informative:1 remove:2 designed:1 plot:1 stationary:25 greedy:1 fewer:1 selected:4 smith:1 core:1 short:1 i... |
2,837 | 3,572 | One Sketch For All: Theory and Application of
Conditional Random Sampling
Ping Li
Dept. of Statistical Science
Cornell University
pingli@cornell.edu
Kenneth W. Church
Microsoft Research
Microsoft Corporation
church@microsoft.com
Trevor J. Hastie
Dept. of Statistics
Stanford University
hastie@stanford.edu
Abstract
Co... | 3572 |@word briefly:1 version:2 norm:13 seems:1 disk:2 d2:3 rgb:1 pick:1 minus:1 moment:3 reduction:4 contains:1 seriously:1 document:2 interestingly:1 outperforms:1 com:1 comparing:2 z2:4 written:1 john:1 realistic:1 limp:1 predetermined:1 kdd:1 analytic:1 designed:1 update:4 selected:2 item:1 record:2 cormode:1 provi... |
2,838 | 3,573 | Modeling the effects of memory on human online
sentence processing with particle filters
Roger Levy
Department of Linguistics
University of California, San Diego
rlevy@ling.ucsd.edu
Florencia Reali Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
{floreali,tom griffiths}@berkeley.edu
A... | 3573 |@word version:1 polynomial:1 seems:4 proportion:4 open:1 rayner:1 harder:1 recursively:1 moment:2 initial:2 prefix:3 crocker:2 past:2 existing:1 reaction:1 current:2 contextual:1 adj:3 reali:1 recovered:1 si:3 yet:2 activation:1 written:1 parsing:25 subsequent:1 resampling:5 cue:3 item:6 beginning:1 ith:1 smith:1... |
2,839 | 3,574 | Multi-label Multiple Kernel Learning
Shuiwang Ji
Arizona State University
Tempe, AZ 85287
shuiwang.ji@asu.edu
Liang Sun
Arizona State University
Tempe, AZ 85287
sun.liang@asu.edu
Rong Jin
Michigan State University
East Lansing, MI 48824
rongjin@cse.msu.edu
Jieping Ye
Arizona State University
Tempe, AZ 85287
jieping... | 3574 |@word norm:1 thereby:2 tr:13 reduction:1 moment:1 score:5 genetic:1 document:2 outperforms:1 existing:2 si:1 numerical:1 partition:1 shape:1 plot:2 intelligence:1 asu:3 ith:1 node:1 cse:1 zhang:1 mathematical:2 constructed:6 differential:1 introductory:1 introduce:1 lansing:1 pairwise:1 p1:1 multi:15 increasing:1... |
2,840 | 3,575 | Deflation Methods for Sparse PCA
Lester Mackey
Computer Science Division
University of California, Berkeley
Berkeley, CA 94703
Abstract
In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization and matr... | 3575 |@word version:1 compression:1 loading:13 norm:1 underperform:1 seek:1 crucially:1 tat:1 decomposition:3 covariance:16 reappearance:1 q1:4 automat:1 substitution:1 series:1 dspca:1 contains:1 bc:1 outperforms:2 must:1 subsequent:2 remove:1 interpretable:1 mackey:1 greedy:2 selected:1 leaf:1 zhang:3 c2:1 direct:1 b... |
2,841 | 3,576 | Grouping Contours Via a Related Image
Praveen Srinivasan
GRASP Laboratory
University of Pennsylvania
Philadelphia, PA 19104
psrin@seas.upenn.edu
Liming Wang
Fudan University
Shanghai, PRC 200433
wanglm@fudan.edu.cn
Jianbo Shi
GRASP Laboratory
University of Pennsylvania
Philadelphia, PA 19104
jshi@cis.upenn.edu
Abst... | 3576 |@word briefly:1 norm:1 seek:4 decomposition:1 shot:1 configuration:1 liu:1 score:3 selecting:4 disparity:3 fevrier:1 tuned:1 comparing:1 yet:2 atop:1 shape:64 cue:3 selected:6 fewer:1 half:1 record:1 colored:4 provides:4 completeness:2 node:2 preference:1 along:2 ijcv:2 scij:3 introduce:1 inter:3 upenn:2 roughly:... |
2,842 | 3,577 | Generative versus discriminative training of RBMs
for classification of fMRI images
Geoffrey E. Hinton
Department of Computer Science
University of Toronto
Toronto, Canada
hinton@cs.toronto.edu
Tanya Schmah
Department of Computer Science
University of Toronto
Toronto, Canada
schmah@cs.toronto.edu
Richard S. Zemel
Dep... | 3577 |@word version:2 seems:1 contrastive:2 noll:1 configuration:1 contains:3 generatively:4 halchenko:1 longitudinal:1 outperforms:2 recovered:1 analysed:1 activation:4 must:1 visible:14 numerical:2 informative:1 chicago:2 partition:4 shape:1 hypothesize:1 discrimination:10 v:14 generative:33 half:4 greedy:1 intellige... |
2,843 | 3,578 | Mixed Membership Stochastic Blockmodels
Edoardo M. Airoldi 1,2 , David M. Blei 1 , Stephen E. Fienberg 3,4 & Eric P. Xing 4?
1
Department of Computer Science, 2 Lewis-Sigler Institute, Princeton University
3
Department of Statistics, 4 School of Computer Science, Carnegie Mellon University
eairoldi@Princeton.EDU
Abst... | 3578 |@word bosco:1 version:1 instrumental:2 sgd:2 carry:1 anthropological:3 necessity:1 series:2 score:2 document:3 existing:1 must:2 john:2 subsequent:1 happen:1 informative:1 numerical:1 partition:1 enables:1 plot:1 interpretable:2 update:9 msb:1 generative:1 instantiate:5 discovering:1 intelligence:1 accordingly:1 ... |
2,844 | 3,579 | A rational model of preference learning
and choice prediction by children
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720, USA
tom griffiths@berkeley.edu
Christopher G. Lucas
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720, USA
clucas@b... | 3579 |@word trial:2 version:1 briefly:1 proportion:2 stronger:1 logit:4 seek:1 simulation:2 recapitulate:1 selecting:3 prefix:1 subjective:3 reaction:1 must:5 john:1 distant:1 subsequent:1 v:1 cue:1 selected:2 fewer:1 item:4 intelligence:1 prize:1 fa9550:1 provides:6 appliance:1 toronto:1 preference:83 simpler:1 five:2... |
2,845 | 358 | Continuous Speech Recognition by
Linked Predictive Neural Networks
Joe Tebelskis, Alex Waibel, Bojan Petek, and Otto Schmidbauer
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We present a large vocabulary, continuous speech recognition system based
on Linked Predictive Neural Net... | 358 |@word version:1 disappointingly:1 noll:1 series:1 score:1 current:2 com:1 lang:1 must:1 discrimination:2 intelligence:1 iso:1 short:2 provides:1 along:2 become:3 specialize:2 consists:2 dialog:1 multi:1 actual:6 window:1 provided:1 what:1 ag:1 corporation:1 every:1 ti:1 classifier:1 qm:1 control:13 positive:3 tend... |
2,846 | 3,580 | Mortal Multi-Armed Bandits
Ravi Kumar
Yahoo! Research
Sunnyvale, CA 94089
ravikumar@yahoo-inc.com
Deepayan Chakrabarti
Yahoo! Research
Sunnyvale, CA 94089
deepay@yahoo-inc.com
Filip Radlinski?
Microsoft Research
Cambridge, UK
filiprad@microsoft.com
Eli Upfal?
Brown University
Providence, RI 02912
eli@cs.brown.edu
Ab... | 3580 |@word exploitation:4 version:4 simulation:2 pick:1 paid:1 minus:1 reduction:2 born:2 efficacy:2 selecting:2 com:3 yet:1 must:3 realistic:3 remove:1 designed:1 update:1 v:1 stationary:1 implying:1 selected:3 greedy:3 warmuth:2 beginning:1 indefinitely:1 provides:3 characterization:1 math:1 teytaud:1 simpler:2 five... |
2,847 | 3,581 | Beyond Novelty Detection: Incongruent Events, when
General and Specific Classifiers Disagree
Abstract
Unexpected stimuli are a challenge to any machine learning algorithm. Here we
identify distinct types of unexpected events, focusing on ?incongruent events? when ?general level? and ?specific level? classifiers give co... | 3581 |@word hippocampus:1 seems:1 solid:1 score:1 past:2 existing:1 current:1 comparing:2 conjunctive:2 must:1 happen:1 v:4 generative:10 cue:2 device:2 item:4 mpm:1 classier:1 short:1 pointer:1 provides:1 detecting:1 node:13 direct:5 scholkopf:1 descendant:1 consists:1 combine:2 compose:1 behavioral:1 manner:1 introdu... |
2,848 | 3,582 | A general framework for investigating how far the
decoding process in the brain can be simplified
Masafumi Oizumi1 , Toshiyuki Ishii2 , Kazuya Ishibashi1
Toshihiko Hosoya2 , Masato Okada1,2
oizumi@mns.k.u-tokyo.ac.jp
tishii@brain.riken.jp,kazuya@mns.k.u-tokyo.ac.jp
hosoya@brain.riken.jp, okada@k.u-tokyo.ac.jp
1
Univer... | 3582 |@word seems:1 proportionality:1 covariance:3 jacob:1 solid:4 carry:4 configuration:1 wako:1 z2:1 comparing:1 written:3 realistic:1 shamai:1 plot:1 short:3 provides:2 constructed:1 ik:3 fitting:1 introduce:2 manner:1 theoretically:2 behavior:1 p1:4 brain:14 discretized:1 actual:2 vertebrate:1 becomes:2 provided:1 ... |
2,849 | 3,583 | A Scalable Hierarchical Distributed Language Model
Andriy Mnih
Department of Computer Science
University of Toronto
amnih@cs.toronto.edu
Geoffrey Hinton
Department of Computer Science
University of Toronto
hinton@cs.toronto.edu
Abstract
Neural probabilistic language models (NPLMs) have been shown to be competitive w... | 3583 |@word version:2 replicate:1 tried:2 covariance:1 mention:1 recursively:2 reduction:5 contains:1 score:4 t7:2 outperforms:1 existing:1 current:4 surprising:1 gauvain:1 assigning:1 john:1 periodically:1 partition:2 christian:1 wanted:1 designed:1 update:1 half:2 leaf:10 inspection:1 ith:1 smith:1 provides:2 draft:1... |
2,850 | 3,584 | Evaluating probabilities under high-dimensional
latent variable models
Iain Murray and Ruslan Salakhutdinov
Department of Computer Science
University of Toronto
Toronto, ON. M5S 3G4. Canada.
{murray,rsalakhu}@cs.toronto.edu
Abstract
We present a simple new Monte Carlo algorithm for evaluating probabilities of
observat... | 3584 |@word version:4 manageable:1 open:1 adrian:1 hyv:1 tried:1 covariance:1 contrastive:2 tr:2 harder:1 initial:1 substitution:1 series:2 contains:3 tuned:1 existing:3 current:2 must:1 dechter:1 visible:5 partition:3 happen:1 cheap:2 christian:1 designed:2 update:2 stationary:9 generative:2 leaf:4 greedy:3 intelligen... |
2,851 | 3,585 | Sparse Online Learning via Truncated Gradient
John Langford
Yahoo! Research
jl@yahoo-inc.com
Lihong Li
Department of Computer Science
Rutgers University
lihong@cs.rutgers.edu
Tong Zhang
Department of Statistics
Rutgers University
tongz@rci.rutgers.edu
Abstract
We propose a general method called truncated gradient to... | 3585 |@word private:1 repository:2 version:5 norm:2 seems:1 dekel:1 sgd:7 recursively:1 reduction:5 wrapper:1 crx:2 vd0:1 spambase:2 current:2 com:1 magic04:2 chu:1 must:1 john:1 informative:1 eleven:1 remove:3 update:8 v:1 implying:1 instantiate:1 warmuth:2 accordingly:1 accepting:1 characterization:1 simpler:1 zhang:... |
2,852 | 3,586 | Adaptive Forward-Backward Greedy Algorithm for
Sparse Learning with Linear Models
Tong Zhang
Statistics Department
Rutgers University, NJ
tzhang@stat.rutgers.edu
Abstract
Consider linear prediction models where the target function is a sparse linear combination of a set of basis functions. We are interested in the pr... | 3586 |@word repository:1 version:4 norm:1 termination:2 simulation:4 pick:3 minus:1 bradley:1 wd:2 surprising:1 attracted:1 partition:1 remove:5 designed:3 plot:1 greedy:57 selected:5 half:1 website:1 beginning:1 completeness:1 boosting:1 zhang:3 five:4 along:3 become:1 incorrect:1 prove:3 introduce:2 theoretically:1 p... |
2,853 | 3,587 | Unifying the Sensory and Motor Components
of Sensorimotor Adaptation
Adrian Haith
School of Informatics
University of Edinburgh, UK
adrian.haith@ed.ac.uk
Carl Jackson
School of Psychology
University of Birmingham, UK
c.p.jackson.1@bham.ac.uk
Chris Miall
School of Psychology
University of Birmingham, UK
r.c.miall@bha... | 3587 |@word trial:43 adrian:2 covariance:2 solid:2 initial:4 series:1 daniel:1 ording:2 existing:1 current:2 surprising:2 must:2 john:1 visible:2 subsequent:2 enables:1 motor:51 plot:1 update:4 v:2 alone:1 cue:5 implying:1 manipulandum:3 nervous:2 accordingly:1 location:10 mathematical:1 along:1 manner:1 ravindran:1 in... |
2,854 | 3,588 | How memory biases affect information transmission:
A rational analysis of serial reproduction
Jing Xu Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720-1650
{jing.xu,tom griffiths}@berkeley.edu
Abstract
Many human interactions involve pieces of information being passed... | 3588 |@word trial:24 middle:1 instruction:1 seek:1 tried:3 brightness:1 accommodate:1 initial:5 score:2 subjective:1 past:1 current:2 elliptical:1 must:1 realize:1 subsequent:2 realistic:1 plot:3 stationary:15 half:3 realizing:1 record:1 provides:5 characterization:2 location:1 five:1 along:4 become:1 combine:1 paragra... |
2,855 | 3,589 | Bayesian Model of Behaviour in Economic Games
Brooks King-Casas
Computational Psychiatry Unit
Baylor College of Medicine.
Houston, TX 77030. USA
bkcasas@cpu.bcm.tmc.edu
Debajyoti Ray
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125. USA
dray@caltech.edu
Peter Dayan
Gatsby Computat... | 3589 |@word private:1 cingulate:1 version:3 proportion:1 norm:1 nd:3 suitably:1 accounting:1 dramatic:1 thereby:1 initial:2 celebrated:1 offering:1 ati:1 subjective:1 past:2 casas:3 current:3 wako:1 activation:1 must:1 subsequent:1 uncooperative:6 statis:1 update:1 alone:1 generative:8 half:2 intelligence:2 beginning:1... |
2,856 | 359 | Connectionist Approaches to the Use of
Markov Models for Speech Recognition
Herve Bourlard t,~
t L & H Speechproducts
Koning Albert 1 laan, 64
1780 Wemmel, BELGIUM
Nelson Morgan ~ I.e Chuck Wooters ~
~ IntI. Compo Sc. Institute
1947, Center St., Suite 600
Berkeley, CA 94704, USA
ABSTRACT
Previous work has shown the ... | 359 |@word sri:1 seems:2 open:1 covariance:1 initial:7 substitution:1 series:1 current:2 contextual:12 must:2 speakerindependent:1 discrimination:2 intelligence:1 leaf:1 beginning:1 compo:1 quantized:6 toronto:1 successive:2 lexicon:1 simpler:2 along:1 prove:1 indeed:2 roughly:1 multi:1 fwm:2 considering:2 provided:3 e... |
2,857 | 3,590 | Continuously-adaptive discretization for
message-passing algorithms
Kannan Achan
Microsoft Research Silicon Valley
Mountain View, California, USA
Michael Isard
Microsoft Research Silicon Valley
Mountain View, California, USA
John MacCormick
Dickinson College
Carlisle, Pennsylvania, USA
Abstract
Continuously-Adaptiv... | 3590 |@word trial:5 msr:1 unaltered:1 coarseness:1 seems:1 tried:1 pick:3 thereby:1 tr:1 accommodate:1 shot:1 recursively:1 initial:1 contains:1 disparity:1 selecting:1 loeliger:1 renewed:1 past:1 outperforms:2 current:5 discretization:49 cad:25 assigning:1 must:4 john:1 subsequent:1 partition:17 additive:1 shape:2 rem... |
2,858 | 3,591 | On the Design of Loss Functions for Classification:
theory, robustness to outliers, and SavageBoost
Hamed Masnadi-Shirazi
Statistical Visual Computing Laboratory,
University of California, San Diego
La Jolla, CA 92039
hmasnadi@ucsd.edu
Nuno Vasconcelos
Statistical Visual Computing Laboratory,
University of California... | 3591 |@word mild:1 version:1 pw:3 prognostic:1 bf:2 open:2 confirms:1 e2v:2 forecaster:1 boundedness:1 liu:1 selecting:3 denoting:1 existing:1 savage:20 comparing:1 current:1 written:4 additive:1 tailoring:1 j1:1 enables:1 plot:1 designed:3 update:1 v:1 fewer:1 selected:3 provides:3 boosting:19 zhang:2 five:1 along:1 d... |
2,859 | 3,592 | Learning Taxonomies by Dependence Maximization
Matthew B. Blaschko
Arthur Gretton
Max Planck Institute for Biological Cybernetics
Spemannstr. 38
72076 T?ubingen, Germany
{blaschko,arthur}@tuebingen.mpg.de
Abstract
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously clust... | 3592 |@word h:1 version:3 inversion:1 norm:5 pulse:1 covariance:4 simplifying:2 elisseeff:1 tr:11 reduction:1 contains:2 score:3 document:4 rkhs:1 existing:1 recovered:2 current:2 assigning:1 must:1 cruz:1 numerical:16 partition:16 informative:3 additive:3 distant:3 motor:1 interpretable:1 update:1 aside:1 greedy:1 dis... |
2,860 | 3,593 | Characterizing neural dependencies
with copula models
Pietro Berkes
Volen Center for Complex Systems
Brandeis University, Waltham, MA 02454
berkes@brandeis.edu
Frank Wood and Jonathan Pillow
Gatsby Computational Neuroscience Unit, UCL
London WC1N 3AR, UK
{fwood,pillow}@gatsby.ucl.ac.uk
Abstract
The coding of informa... | 3593 |@word trial:1 version:1 proportion:1 nd:1 open:1 covariance:3 score:2 selecting:1 interestingly:1 must:2 readily:2 fn:7 numerical:1 visible:1 partition:1 shape:1 motor:4 visibility:1 treating:1 plot:3 remove:1 generative:2 selected:1 characterization:1 zhang:1 combine:2 inside:1 pairwise:2 inter:1 examine:1 multi... |
2,861 | 3,594 | Support Vector Machines with a Reject Option
Yves Grandvalet 1, 2 , Alain Rakotomamonjy 3 , Joseph Keshet 2 and St?ephane Canu 3
1
2
Heudiasyc, UMR CNRS 6599
Idiap Research Institute
Universit?e de Technologie de Compi`egne
Centre du Parc
BP 20529, 60205 Compi`egne Cedex, France CP 592, CH-1920 Martigny Switzerland
3
... | 3594 |@word mild:1 trial:3 illustrating:1 version:1 repository:1 norm:1 incurs:1 solid:1 series:3 score:4 selecting:1 disparity:1 current:1 mari:1 exy:4 assigning:1 si:4 written:1 fn:3 partition:5 designed:1 update:1 v:1 greedy:1 fewer:2 devising:1 selected:1 accordingly:1 eminent:1 egne:2 provides:2 location:1 attack:... |
2,862 | 3,595 | Localized Sliced Inverse Regression
Qiang Wu, Sayan Mukherjee
Department of Statistical Science
Institute for Genome Sciences & Policy
Department of Computer Science
Duke University, Durham
NC 27708-0251, U.S.A
{qiang, sayan}@stat.duke.edu
Feng Liang
Department of Statistics
University of Illinois at Urbana-Champaign... | 3595 |@word version:6 underline:1 tamayo:1 simulation:3 covariance:13 decomposition:3 moment:3 reduction:32 liu:1 contains:2 loc:8 selecting:1 existing:2 comparing:2 com:1 si:3 must:1 realize:1 designed:2 plot:1 intelligence:1 cook:3 provides:2 intellectual:1 downing:1 mathematical:1 along:1 isds:1 introduce:2 expected... |
2,863 | 3,596 | Robust Regression and Lasso
Huan Xu
Department of Electrical and Computer Engineering
McGill University
Montreal, QC Canada
xuhuan@cim.mcgill.ca
Constantine Caramanis
Department of Electrical and Computer Engineering
The University of Texas at Austin
Austin, Texas
cmcaram@ece.utexas.edu
Shie Mannor
Department of Elect... | 3596 |@word version:5 norm:16 seek:1 decomposition:1 series:1 bhattacharyya:1 ka:7 yet:2 attracted:1 john:2 tenet:1 subsequent:1 girosi:1 remove:1 generative:6 ith:3 provides:2 mannor:3 org:1 zhang:1 unbounded:1 along:1 direct:3 prove:8 introduce:1 coifman:1 x0:5 expected:2 indeed:2 cand:1 considering:1 ua:2 underlying... |
2,864 | 3,597 | Large Margin Taxonomy Embedding with an
Application to Document Categorization
Olivier Chapelle
Yahoo! Research
chap@yahoo-inc.com
Kilian Weinberger
Yahoo! Research
kilian@yahoo-inc.com
Abstract
Applications of multi-class classification, such as document categorization, often
appear in cost-sensitive settings. Rece... | 3597 |@word proceeded:1 cox:2 briefly:1 version:2 seems:1 open:1 decomposition:2 thereby:1 reduction:2 liu:1 score:2 document:48 com:2 assigning:1 written:1 stemming:1 mesh:5 hofmann:1 v:3 short:1 core:1 blei:1 node:7 attack:2 org:1 five:1 rc:8 c2:1 constructed:1 consists:2 absorbs:1 inside:1 paragraph:1 introduce:1 in... |
2,865 | 3,598 | Multi-Level Active Prediction of Useful Image
Annotations for Recognition
Sudheendra Vijayanarasimhan and Kristen Grauman
Department of Computer Sciences
University of Texas at Austin
{svnaras,grauman}@cs.utexas.edu
Abstract
We introduce a framework for actively learning visual categories from a mixture of
weakly and... | 3598 |@word trial:2 illustrating:1 middle:1 manageable:1 seems:1 stronger:1 flach:1 seek:2 propagate:1 ratan:1 accounting:1 thereby:1 tr:2 accommodate:3 reduction:2 initial:9 contains:8 selecting:1 interestingly:1 outperforms:3 existing:3 horvitz:1 current:4 z2:1 must:9 partition:2 informative:5 midway:1 shape:1 hofman... |
2,866 | 3,599 | DiscLDA: Discriminative Learning for
Dimensionality Reduction and Classification
Simon Lacoste-Julien
Computer Science Division
UC Berkeley
Berkeley, CA 94720
Fei Sha
Dept. of Computer Science
University of Southern California
Los Angeles, CA 90089
Michael I. Jordan
Dept. of EECS and Statistics
UC Berkeley
Berkeley,... | 3599 |@word version:3 proportion:7 disk:1 plsa:2 tried:1 uncovers:1 dealer:1 pick:1 tr:3 reduction:14 initial:1 electronics:1 contains:2 murder:1 tuned:1 document:45 amp:1 current:1 wd:4 scatter:1 john:1 partition:1 enables:1 christian:5 plot:1 interpretable:1 update:1 v:1 alone:1 generative:8 discovering:2 cook:1 inte... |
2,867 | 36 | 554
STABILITY RESULTS FOR NEURAL NETWORKS
A. N. Michell, J. A. FarreUi , and W. Porod 2
Department of Electrical and Computer Engineering
University of Notre Dame
Notre Dame, IN 46556
ABSTRACT
In the present paper we survey and utilize results from the qualitative theory of large
scale interconnected dynamical systems... | 36 |@word inversion:1 initial:1 contains:2 com:1 attracted:1 must:2 ixil:2 subsequent:1 enables:1 designed:1 leaf:1 provides:3 location:1 bixi:2 successive:2 along:4 c2:1 differential:2 qualitative:14 prove:4 lj2:1 manner:3 introduce:1 behavior:2 themselves:1 frequently:3 automatically:1 increasing:1 provided:3 estimat... |
2,868 | 360 | Speech Recognition
Using Demi-Syllable Neural Prediction Model
Ken-ichi Iso and Takao Watanabe
C & C Information Technology Research Laboratories
NEC Corporation
4-1-1 Miyazaki, Miyamae-ku, Kawasaki 213, JAPAN
Abstract
The Neural Prediction Model is the speech recognition model based on
pattern prediction by multilay... | 360 |@word effect:1 implemented:2 predicted:1 involves:1 validity:1 february:1 former:2 objective:1 correct:1 heuristically:1 closure:1 laboratory:2 vc:1 occurs:1 adjacent:2 dp:6 quiet:1 distance:2 speaker:7 mel:1 thank:1 takao:1 hmm:2 criterion:1 configuration:8 concatenation:3 complete:1 confusion:1 subword:11 ka:1 c... |
2,869 | 3,600 | Adapting to a Market Shock: Optimal Sequential
Market-Making
Malik Magdon-Ismail
Department of Computer Science
Rensselaer Polytechnic Institute
Troy, NY 12180
magdon@cs.rpi.edu
Sanmay Das
Department of Computer Science
Rensselaer Polytechnic Institute
Troy, NY 12180
sanmay@cs.rpi.edu
Abstract
We study the profit-ma... | 3600 |@word exploitation:3 illustrating:1 heterogeneously:1 tedious:1 willing:6 simulation:6 dealer:2 p0:1 profit:44 solid:1 reduction:1 initial:4 liquid:1 offering:2 renewed:1 bootstrapped:1 outperforms:1 comparing:1 surprising:3 rpi:2 yet:1 dx:3 must:3 gv:1 update:17 v:5 alone:1 greedy:1 prohibitive:1 intelligence:2 ... |
2,870 | 3,601 | Stress, noradrenaline, and realistic prediction of
mouse behaviour using reinforcement learning
Gediminas Luk?sys1,2 , Carmen Sandi2 , Wulfram Gerstner1
1
Laboratory of Computational Neuroscience
2
Laboratory of Behavioural Genetics
Ecole Polytechnique F?ed?erale de Lausanne (EPFL)
Lausanne, CH-1015, Switzerland
{gedim... | 3601 |@word luk:1 exploitation:17 cingulate:1 noradrenergic:8 trial:10 loading:1 hippocampus:1 open:1 additively:1 simulation:4 dba:11 pick:4 reduction:1 initial:2 responsivity:1 selecting:1 ecole:1 genetic:4 comparing:3 anterior:1 marquardt:2 activation:1 yet:1 realistic:3 subsequent:1 happen:1 plasticity:1 numerical:... |
2,871 | 3,602 | Dimensionality Reduction for Data in Multiple
Feature Representations
Yen-Yu Lin1,2
Tyng-Luh Liu1
Chiou-Shann Fuh2
1
Institute of Information Science, Academia Sinica, Taipei, Taiwan
{yylin, liutyng}@iis.sinica.edu.tw
2
Department of CSIE, National Taiwan University, Taipei, Taiwan
fuh@csie.ntu.edu.tw
Abstract
In sol... | 3602 |@word seems:1 km:16 tried:1 lpp:1 pick:1 accommodate:1 carry:1 reduction:18 initial:2 liu:2 contains:1 exclusively:1 rkhs:3 ala:1 existing:2 current:1 comparing:1 tackling:1 must:1 written:1 academia:1 blur:1 shape:3 designed:1 depict:1 xdx:1 cue:1 generative:2 guess:2 phog:3 record:1 provides:2 zhang:7 construct... |
2,872 | 3,603 | A spatially varying two-sample recombinant
coalescent, with applications to HIV escape response
Alexander Braunstein
Statistics Department
University of Pennsylvania
Wharton School
Philadelphia, PA 19104
braunsf@wharton.upenn.edu
Zhi Wei
Computer Science Department
New Jersey Institute of Technology
Newark, NJ 07102
... | 3603 |@word briefly:1 moment:1 substitution:3 contains:2 genetic:1 seriously:1 rightmost:1 current:4 virus:8 yet:1 must:1 schierup:2 numerical:1 designed:1 plot:2 update:6 fewer:1 accordingly:1 ith:1 positionally:1 detecting:1 location:3 simpler:1 five:1 phylogenetic:1 along:6 constructed:1 profound:1 replication:2 con... |
2,873 | 3,604 | Integrating locally learned causal structures
with overlapping variables
Robert E. Tillman
Carnegie Mellon University
Pittsburgh, PA 15213
rtillman@andrew.cmu.edu
David Danks, Clark Glymour
Carnegie Mellon University &
Institute for Human & Machine Cognition
Pittsburgh, PA 15213
{ddanks,cg09}@andrew.cmu.edu
Abstract... | 3604 |@word version:1 nd:1 sex:4 open:1 d2:1 simulation:1 propagate:1 decomposition:1 reduction:1 initial:1 score:2 united:2 ramsey:1 existing:2 current:5 comparing:1 surprising:1 must:2 readily:1 concatenate:1 happen:1 informative:1 remove:4 greedy:1 discovering:2 fewer:1 tillman:1 intelligence:4 smith:1 record:1 poin... |
2,874 | 3,605 | Online Optimization in X -Armed Bandits
S?ebastien Bubeck
INRIA Lille, SequeL project, France
R?emi Munos
INRIA Lille, SequeL project, France
sebastien.bubeck@inria.fr
remi.munos@inria.fr
Gilles Stoltz
Ecole Normale Sup?erieure and HEC Paris
Csaba Szepesv?ari
Department of Computing Science, University of Alberta... | 3605 |@word trial:1 middle:1 norm:2 open:5 simulation:1 hec:1 bn:2 forecaster:2 p0:1 pick:3 recursively:1 contains:4 selecting:2 ecole:1 past:3 subsequent:1 partition:1 shape:1 enables:1 fund:1 intelligence:1 selected:3 instantiate:1 node:46 teytaud:1 along:5 c2:1 symposium:1 descendant:5 prove:5 introduce:1 manner:3 i... |
2,875 | 3,606 | Dependent Dirichlet Process Spike Sorting
? Yee Whye Teh
Jan Gasthaus, Frank Wood, Dilan G?orur,
Gatsby Computational Neuroscience Unit
University College London
London, WC1N 3AR, UK
{j.gasthaus, fwood, dilan, ywteh}@gatsby.ucl.ac.uk
Abstract
In this paper we propose a new incremental spike sorting model that automat... | 3606 |@word neurophysiology:3 briefly:1 middle:1 hippocampus:2 simulation:1 covariance:1 reduction:2 contains:1 ecole:1 outperforms:1 current:1 assigning:3 must:2 fn:3 visible:1 interspike:4 shape:7 remove:2 plot:1 jenson:1 n0:5 update:1 resampling:2 stationary:2 half:1 device:2 intelligence:1 accordingly:1 isotropic:1... |
2,876 | 3,607 | Kernel-ARMA for Hand Tracking and
Brain-Machine Interfacing During 3D Motor Control
Lavi Shpigelman1 , Hagai Lalazar 2 and Eilon Vaadia 3
Interdisciplinary Center for Neural Computation
The Hebrew University of Jerusalem, Israel
1
shpigi@gmail.com, 2 hagai@alice.nc.huji.ac.il,
3
eilonv@ekmd.huji.ac.il
Abstract
Using ... | 3607 |@word trial:15 middle:1 version:6 norm:1 seems:3 open:4 tried:3 simplifying:1 initial:3 series:3 score:3 selecting:2 outperforms:1 current:5 com:1 comparing:1 ka:1 gmail:1 yet:1 must:2 scatter:3 conforming:1 luis:1 concatenate:2 informative:1 motor:12 reappeared:1 plot:6 update:1 v:2 alone:1 half:1 selected:2 dev... |
2,877 | 3,608 | Kernelized Sorting
Novi Quadrianto
RSISE, ANU & SML, NICTA
Canberra, ACT, Australia
novi.quad@gmail.com
Le Song
SCS, CMU
Pittsburgh, PA, USA
lesong@cs.cmu.edu
Alex J. Smola
Yahoo! Research
Santa Clara, CA, USA
alex@smola.org
Abstract
Object matching is a fundamental operation in data analysis. It typically requires... | 3608 |@word repository:1 version:5 norm:5 seek:1 rgb:1 covariance:4 commute:1 pick:1 tr:7 reduction:1 initial:1 score:2 hardy:1 document:21 rkhs:2 existing:1 blank:1 com:3 recovered:1 clara:1 gmail:1 exy:2 written:1 yet:1 portuguese:1 stemming:2 mesh:1 subsequent:1 hofmann:1 remove:1 drop:1 update:1 generative:2 half:5... |
2,878 | 3,609 | Estimating the Location and Orientation of Complex,
Correlated Neural Activity using MEG
D.P. Wipf, J.P. Owen, H.T. Attias, K. Sekihara, and S.S. Nagarajan
Biomagnetic Imaging Laboratory
University of California, San Francisco
Abstract
The synchronous brain activity measured via MEG (or EEG) can be interpreted
as ari... | 3609 |@word trial:3 determinant:2 middle:5 advantageous:1 norm:2 nd:1 open:1 m100:2 simulation:4 covariance:14 decomposition:1 accounting:1 configuration:5 contains:1 efficacy:1 score:1 outperforms:1 existing:4 current:8 recovered:1 si:7 activation:4 must:3 import:1 readily:1 kiebel:1 realistic:2 wanted:1 remove:1 desi... |
2,879 | 361 | EMPATH: Face, Emotion, and Gender Recognition Using Holons
Munro & Zipser (1987) showed that a back propagation network could be used
compression. The network is trained to simply reproduce its input, and so can
as a non-linear version of Kohonen's (1977) auto-associator. However it must
through a narrow channel of hi... | 361 |@word version:1 judgement:1 compression:7 sex:2 simulation:1 covariance:1 eng:1 brightness:1 tr:1 carry:1 reduction:1 initial:2 empath:5 past:2 activation:3 must:3 cottrell:7 distant:1 rward:1 informative:1 extensional:1 discrimination:7 v:2 selected:1 es:1 detecting:1 sigmoidal:1 along:1 differential:1 become:1 c... |
2,880 | 3,610 | Cell Assemblies in Large Sparse Inhibitory Networks
of Biologically Realistic Spiking Neurons
Jeff Wickens
OIST, Uruma, Okinawa, Japan.
wickens@oist.jp
Adam Ponzi
OIST, Uruma, Okinawa, Japan.
adamp@oist.jp
Abstract
Cell assemblies exhibiting episodes of recurrent coherent activity have been
observed in several brain... | 3610 |@word hippocampus:3 proportion:8 hyperpolarized:1 confirms:1 simulation:16 tried:1 lobe:1 excited:3 postsynaptically:1 solid:1 reduction:1 initial:1 series:20 efficacy:1 interestingly:3 current:12 neurophys:2 analysed:1 activation:4 attracted:1 cruz:1 interrupted:2 realistic:8 numerical:3 periodically:5 physiol:3... |
2,881 | 3,611 | PSDBoost: Matrix-Generation Linear Programming
for Positive Semidefinite Matrices Learning
Chunhua Shen?? , Alan Welsh? , Lei Wang?
NICTA Canberra Research Lab, Canberra, ACT 2601, Australia?
?
Australian National University, Canberra, ACT 0200, Australia
?
Abstract
In this work, we consider the problem of learning ... | 3611 |@word mild:1 kulis:1 repository:1 version:2 compression:1 open:1 hu:1 decomposition:3 kent:1 tr:12 contains:2 series:1 zij:1 denoting:2 ours:1 psdboost:17 existing:1 current:6 com:1 optim:1 attracted:1 must:6 written:1 numerical:3 remove:1 designed:2 drop:1 update:2 greedy:4 fewer:1 selected:3 indefinitely:1 prov... |
2,882 | 3,612 | Theory of matching pursuit
Zakria Hussain and John Shawe-Taylor
Department of Computer Science
University College London, UK
{z.hussain,j.shawe-taylor}@cs.ucl.ac.uk
Abstract
We analyse matching pursuit for kernel principal components analysis (KPCA)
by proving that the sparse subspace it produces is a sample compress... | 3612 |@word version:3 compression:51 norm:5 seems:1 pick:1 tr:10 nystr:2 carry:1 reduction:1 substitution:1 chervonenkis:3 denoting:1 existing:1 comparing:1 analysed:2 si:2 john:1 cruz:2 plot:12 sponsored:1 greedy:3 warmuth:2 plane:1 xk:1 ith:5 hyperplanes:2 zhang:1 constructed:2 become:1 ik:15 prove:2 redefine:1 manne... |
2,883 | 3,613 | Improving on Expectation Propagation
Manfred Opper
Computer Science, TU Berlin
opperm@cs.tu-berlin.de
Ulrich Paquet
Computer Laboratory, University of Cambridge
ulrich@cantab.net
Ole Winther
Informatics and Mathematical Modelling, Technical University of Denmark
owi@imm.dtu.dk
Abstract
A series of corrections is de... | 3613 |@word determinant:1 msr:1 inversion:1 polynomial:5 seems:1 nd:2 confirms:1 simulation:1 covariance:7 simplifying:1 tr:1 outlook:1 harder:1 ld:2 moment:6 initial:1 series:7 contains:1 comparing:1 surprising:1 dx:6 ikeda:1 fn:13 subsequent:1 partition:10 tilted:2 plot:4 v:1 leaf:1 vanishing:1 manfred:1 normalising:... |
2,884 | 3,614 | An interior-point stochastic approximation
method and an L1-regularized delta rule
Peter Carbonetto
pcarbo@cs.ubc.ca
Mark Schmidt
schmidtm@cs.ubc.ca
Nando de Freitas
nando@cs.ubc.ca
Department of Computer Science
University of British Columbia
Vancouver, B.C., Canada V6T 1Z4
Abstract
The stochastic approximation m... | 3614 |@word trial:1 middle:1 briefly:1 seems:1 norm:2 nd:3 open:1 simulation:1 tried:1 decomposition:1 contrastive:1 thereby:1 catastrophically:1 initial:1 series:2 zij:2 document:1 freitas:1 existing:3 recovered:1 yet:1 written:1 must:3 john:1 subsequent:1 happen:1 thrust:1 numerical:2 shape:1 hypothesize:1 treating:1... |
2,885 | 3,615 | Structure Learning in Human Sequential
Decision-Making
?
Daniel Acuna
Dept. of Computer Science and Eng.
University of Minnesota?Twin Cities
acuna002@umn.edu
Paul Schrater
Dept. of Psychology and Computer Science and Eng.
University of Minnesota?Twin Cities
schrater@umn.edu
Abstract
We use graphical models and struc... | 3615 |@word trial:4 exploitation:4 proportion:6 rigged:1 instruction:1 seek:1 simulation:3 eng:2 brightness:1 dramatic:1 pick:1 solid:1 reduction:1 contains:1 score:1 daniel:2 recovered:1 current:1 comparing:1 si:2 must:2 john:1 partition:1 analytic:1 drop:1 plot:2 sundaram:1 v:2 alone:1 greedy:1 generative:3 fewer:1 c... |
2,886 | 3,616 | Nonparametric Regression and Classification with
Joint Sparsity Constraints
Han Liu John Lafferty
Larry Wasserman
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our approach is base... | 3616 |@word multitask:1 version:7 middle:3 norm:19 turlach:2 calculus:3 km:1 simulation:2 hu:2 solid:1 liu:1 series:1 score:5 t7:2 denoting:2 tuned:1 interestingly:1 fbj:10 com:1 john:1 additive:24 numerical:1 informative:1 enables:1 plot:4 v:1 stationary:1 intelligence:1 selected:7 xk:2 district:1 c6:4 zhang:5 c2:4 di... |
2,887 | 3,617 | Hierarchical Fisher Kernels for Longitudinal Data
Zhengdong Lu Todd K. Leen
Dept. of Computer Science & Engineering
Oregon Health & Science University
Beaverton, OR 97006
luz@cs.utexas.edu,tleen@csee.ogi.edu
Jeffrey Kaye
Layton Aging & Alzheimer?s Disease Center
Oregon Health & Science University
Portland, OR 97201
ka... | 3617 |@word mild:1 middle:1 kondor:1 polynomial:4 proportion:1 km:3 covariance:4 pavel:1 fifteen:1 series:5 score:3 denoting:1 ours:1 longitudinal:7 yni:2 existing:1 dx:1 must:1 additive:1 enables:1 motor:8 plot:3 designed:1 v:5 generative:20 parameterization:1 slowing:2 oldest:2 parametrization:1 ith:1 sys:1 detecting... |
2,888 | 3,618 | Improved Moves for Truncated Convex Models
M. Pawan Kumar
Dept. of Engineering Science
University of Oxford
P.H.S. Torr
Dept. of Computing
Oxford Brookes University
pawan@robots.ox.ac.uk
philiptorr@brookes.ac.uk
Abstract
We consider the problem of obtaining the approximate maximum a posteriori estimate of a discre... | 3618 |@word kohli:1 open:1 termination:1 d2:1 decomposition:1 thereby:1 inpainting:1 initial:1 contains:2 series:2 disparity:4 relabelled:1 denoting:1 current:2 surprising:1 must:1 additive:1 subsequent:1 partition:1 designed:1 mccallum:1 provides:13 math:1 revisited:1 treereweighted:1 five:3 relabelling:1 naor:1 intro... |
2,889 | 3,619 | A computational model of hippocampal function in
trace conditioning
Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek
Department of Computing Science
University of Alberta
Edmonton, AB, Canada T6G 2E8
{elliot,sutton,everbeek}@cs.ualberta.ca
E. James Kehoe
School of Psychology
University of New South Wales
Sydney, NSW... | 3619 |@word neurophysiology:1 trial:12 middle:4 eliminating:2 version:1 hippocampus:14 seems:1 termination:1 simulation:3 bvt:1 nsw:1 solid:2 configuration:2 series:6 contains:1 ours:1 existing:1 current:2 contextual:2 comparing:1 activation:2 yet:1 plasticity:1 asymptote:2 remove:1 drop:2 update:2 discrimination:1 cue... |
2,890 | 362 | A Second-Order Translation, Rotation and
Scale Invariant Neural Network
Shelly D.D. Goggin
Kristina M. Johnson
Karl E. Gustafson?
Optoelectronic Computing Systems Center and
Department of Electrical and Computer Engineering
University of Colorado at Boulder
Boulder, CO 80309
shellg@boulder.colorado.edu
ABSTRACT
A sec... | 362 |@word especially:1 concept:1 wedge:26 f4:3 simulation:2 fa:1 exploration:1 ll:2 width:2 distance:1 higherorder:2 require:1 mapped:1 reduction:1 hong:1 f1:2 generalization:1 microstructure:1 evenly:1 review:1 biological:1 performs:2 assuming:1 o1:1 lkl:1 image:19 activation:2 fi:1 cognition:1 readily:1 rotation:17 ... |
2,891 | 3,620 | Learning Hybrid Models for Image Annotation with
Partially Labeled Data
Xuming He
Department of Statistics
UCLA
hexm@stat.ucla.edu
Richard S. Zemel
Department of Computer Science
University of Toronto
zemel@cs.toronto.edu
Abstract
Extensive labeled data for image annotation systems, which learn to assign class
label... | 3620 |@word version:2 middle:2 proportion:6 stronger:3 triggs:2 tedious:1 lnh:2 gradual:1 rgb:1 textonboost:1 tuned:1 contextual:2 yet:1 written:2 john:2 sanjiv:1 partition:4 shape:1 update:2 grass:2 stationary:1 generative:10 fewer:1 cue:3 item:1 mpm:1 plane:5 mccallum:2 blei:2 provides:2 node:2 toronto:2 location:1 s... |
2,892 | 3,621 | Analyzing human feature learning as
nonparametric Bayesian inference
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Tom Griffiths@berkeley.edu
Joseph L. Austerweil
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
Joseph.Austerweil@gmail... | 3621 |@word nd:1 confirms:1 simulation:7 seek:2 covariance:1 brightness:1 tr:1 accommodate:1 configuration:1 selecting:1 past:1 existing:1 com:1 comparing:3 gmail:1 must:1 parsing:1 alphanumeric:1 informative:1 designed:1 zik:2 discrimination:1 cue:3 intelligence:1 prespecified:1 provides:5 contribute:1 five:1 unbounde... |
2,893 | 3,622 | The Mondrian Process
Daniel M. Roy
Massachusetts Institute of Technology
Yee Whye Teh
Gatsby Unit, University College London
droy@mit.edu
ywteh@gatsby.ucl.ac.uk
Abstract
We describe a novel class of distributions, called Mondrian processes, which
can be interpreted as probability distributions over kd-tree data str... | 3622 |@word nd:1 open:1 calculus:1 simulation:2 uncovers:1 serie:1 initial:1 contains:1 daniel:1 janson:2 interestingly:1 recovered:1 comparing:1 dx:4 must:2 written:1 john:1 import:1 academia:1 partition:58 stationary:1 generative:6 leaf:4 intelligence:4 item:2 plane:1 smith:2 yamada:1 guillotine:3 math:1 node:1 evy:3... |
2,894 | 3,623 | Adaptive Martingale Boosting
Philip M. Long
Google
plong@google.com
Rocco A. Servedio
Columbia University
rocco@cs.columbia.edu
Abstract
In recent work Long and Servedio [LS05] presented a ?martingale boosting? algorithm that works by constructing a branching program over weak classifiers and
has a simple analysis b... | 3623 |@word version:1 briefly:3 polynomial:1 simulation:1 initial:3 rightmost:2 past:2 current:1 com:1 must:2 subsequent:1 numerical:1 fewer:4 farther:1 boosting:46 complication:1 location:12 successive:2 denis:1 node:60 along:2 constructed:8 direct:1 c2:1 specialize:1 interscience:1 x0:1 expected:1 indeed:1 behavior:1... |
2,895 | 3,624 | Generative and Discriminative Learning with
Unknown Labeling Bias
Miroslav Dud??k
Carnegie Mellon University
5000 Forbes Ave, Pittsburgh, PA 15213
Steven J. Phillips
AT&T Labs ? Research
180 Park Ave, Florham Park, NJ 07932
mdudik@cmu.edu
phillips@research.att.com
Abstract
We apply robust Bayesian decision theory ... | 3624 |@word version:2 proportion:8 accounting:1 moment:5 herbarium:2 contains:4 att:1 tuned:1 rightmost:1 outperforms:1 existing:1 ferrier:1 com:1 jaynes:1 plot:1 discrimination:2 alone:2 generative:13 selected:1 intelligence:1 short:1 record:2 sudden:1 boosting:1 location:11 oak:1 along:1 consists:2 shorthand:1 prove:... |
2,896 | 3,625 | Stochastic Relational Models for
Large-scale Dyadic Data using MCMC
Shenghuo Zhu
Kai Yu
Yihong Gong
NEC Laboratories America, Cupertino, CA 95014, USA
{zsh, kyu, ygong}@sv.nec-labs.com
Abstract
Stochastic relational models (SRMs) [15] provide a rich family of choices for
learning and predicting dyadic data between tw... | 3625 |@word determinant:1 nd:4 d2:3 covariance:10 dramatic:1 reduction:1 inefficiency:1 contains:2 liu:1 interestingly:1 outperforms:2 com:2 comparing:1 wd:2 chu:1 must:1 written:4 numerical:1 informative:5 kdd:1 hofmann:1 noninformative:1 update:2 v:2 half:1 generative:5 intelligence:2 item:3 accordingly:1 yamada:1 re... |
2,897 | 3,626 | A Convergent O(n) Algorithm
for Off-policy Temporal-difference Learning
with Linear Function Approximation
Richard S. Sutton, Csaba Szepesv?ari?, Hamid Reza Maei
Reinforcement Learning and Artificial Intelligence Laboratory
Department of Computing Science
University of Alberta
Edmonton, Alberta, Canada T6G 2E8
Abstra... | 3626 |@word kgk:1 determinant:1 version:1 norm:3 c0:2 twelfth:2 r:1 simulation:1 boundedness:1 moment:2 initial:1 series:1 selecting:1 denoting:1 existing:2 kmk:1 current:3 written:1 readily:1 visible:1 wiewiora:1 shape:1 remove:1 update:9 fund:1 stationary:4 intelligence:5 greedy:2 fewer:1 selected:2 offpolicy:1 accor... |
2,898 | 3,627 | The Infinite Hierarchical Factor Regression Model
Piyush Rai and Hal Daum?e III
School of Computing, University of Utah
{piyush,hal}@cs.utah.edu
Abstract
We propose a nonparametric Bayesian factor regression model that accounts for
uncertainty in the number of factors, and the relationship between factors. To
accompl... | 3627 |@word cu:1 middle:1 loading:23 open:1 covariance:1 prominence:1 tr:1 moment:1 initial:1 configuration:1 contains:1 efficacy:1 selecting:2 genetic:1 ours:1 past:2 existing:3 current:1 comparing:1 recovered:1 written:1 romance:1 realistic:1 partition:1 designed:1 plot:1 update:1 zik:4 v:2 greedy:1 discovering:1 sel... |
2,899 | 3,628 | Kernel Methods for Deep Learning
Youngmin Cho and Lawrence K. Saul
Department of Computer Science and Engineering
University of California, San Diego
9500 Gilman Drive, Mail Code 0404
La Jolla, CA 92093-0404
{yoc002,saul}@cs.ucsd.edu
Abstract
We introduce a new family of positive-definite kernel functions that mimic ... | 3628 |@word multitask:1 nificantly:1 version:1 briefly:1 polynomial:7 norm:6 d2:1 cos2:1 elisseeff:1 solid:1 contains:3 interestingly:1 current:1 com:1 activation:7 yet:1 intriguing:1 dw1:1 goldberger:1 wx:1 j1:1 informative:1 hypothesize:5 designed:2 hoping:1 greedy:2 plane:3 ith:2 record:1 hypersphere:5 quantizer:1 s... |
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