Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
6,900 | 7,241 | The Scaling Limit of High-Dimensional Online
Independent Component Analysis
Chuang Wang and Yue M. Lu
John A. Paulson School of Engineering and Applied Sciences
Harvard University
33 Oxford Street, Cambridge, MA 02138, USA
{chuangwang,yuelu}@seas.harvard.edu
Abstract
We analyze the dynamics of an online algorithm for... | 7241 |@word trial:1 briefly:1 norm:4 hyv:1 simulation:3 decomposition:2 covariance:1 contraction:1 carry:1 moment:1 initial:5 liu:1 series:1 interestingly:2 amp:1 existing:1 reaction:1 nt:10 dx:4 attracted:1 written:1 john:2 must:4 numerical:6 informative:4 plot:2 update:3 generative:2 guess:2 qnt:5 trapping:1 xk:52 it... |
6,901 | 7,242 | Approximation Algorithms for
`0-Low Rank Approximation
Karl Bringmann1
kbringma@mpi-inf.mpg.de
1
Pavel Kolev1?
pkolev@mpi-inf.mpg.de
David P. Woodruff2
dwoodruf@cs.cmu.edu
Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbr?cken, Germany
2
Department of Computer Science, Carnegie Mellon Univers... | 7242 |@word version:1 radim:1 polynomial:6 norm:21 seems:1 compression:2 km:11 seek:2 vek:1 bn:2 decomposition:4 pavel:1 eng:2 nsw:1 incurs:1 asks:3 bicriteria:6 reduction:1 configuration:1 contains:4 selecting:3 woodruff:7 daniel:1 document:2 ka:47 written:2 must:1 john:2 numerical:2 partition:2 razenshteyn:1 cheap:1 ... |
6,902 | 7,243 | The power of absolute discounting:
all-dimensional distribution estimation
Moein Falahatgar
UCSD
moein@ucsd.edu
Mesrob Ohannessian
TTIC
mesrob@gmail.com
Alon Orlitsky
UCSD
alon@ucsd.edu
Venkatadheeraj Pichapati
UCSD
dheerajpv7@ucsd.edu
Abstract
Categorical models are a natural fit for many problems. When learning t... | 7243 |@word version:5 polynomial:1 compression:4 stronger:1 simulation:1 unbeatable:1 paid:1 mammal:1 mention:3 jafarpour:2 celebrated:2 contains:2 series:1 interestingly:1 outperforms:1 current:3 com:1 comparing:2 gmail:1 written:1 john:1 additive:1 happen:1 informative:1 plot:1 designed:1 n0:4 aside:1 v:2 accordingly... |
6,903 | 7,244 | Few-Shot Adversarial Domain Adaptation
Saeid Motiian, Quinn Jones, Seyed Mehdi Iranmanesh, Gianfranco Doretto
Lane Department of Computer Science and Electrical Engineering
West Virginia University
{samotian, qjones1, seiranmanesh, gidoretto}@mix.wvu.edu
Abstract
This work provides a framework for addressing the prob... | 7244 |@word h:3 kulis:2 cnn:2 version:1 middle:1 everingham:1 hu:1 tenka:1 rgb:2 prominence:1 shot:7 reduction:1 initial:1 liu:1 contains:1 selecting:1 salzmann:2 document:1 outperforms:1 activation:7 scatter:1 concatenate:1 happen:1 realistic:1 update:4 generative:11 selected:3 intelligence:2 uda:22 scotland:1 bissacc... |
6,904 | 7,245 | Spectral Mixture Kernels for
Multi-Output Gaussian Processes
Gabriel Parra
Department of Mathematical Engineering
Universidad de Chile
gparra@dim.uchile.cl
Felipe Tobar
Center for Mathematical Modeling
Universidad de Chile
ftobar@dim.uchile.cl
Abstract
Early approaches to multiple-output Gaussian processes (MOGPs) r... | 7245 |@word middle:1 version:5 smirnov:1 r:2 covariance:68 decomposition:3 solid:2 igp:2 contains:1 series:3 outperforms:1 existing:2 imaginary:2 current:1 dx:1 gpu:1 multioutput:4 concatenate:1 realistic:1 designed:2 interpretable:1 stationary:11 generative:1 half:1 accordingly:1 chile:3 ith:4 ksm:2 dissertation:1 pro... |
6,905 | 7,246 | Neural Expectation Maximization
Klaus Greff?
IDSIA
klaus@idsia.ch
Sjoerd van Steenkiste?
IDSIA
sjoerd@idsia.ch
J?rgen Schmidhuber
IDSIA
juergen@idsia.ch
Abstract
Many real world tasks such as reasoning and physical interaction require identi?cation and manipulation of conceptual entities. A ?rst step towards solving... | 7246 |@word middle:1 version:2 compression:1 seems:2 nd:10 bptt:1 reused:1 open:1 pieter:1 hyv:1 simulation:1 carry:1 contains:1 score:14 daniel:1 precluding:1 envision:1 current:5 comparing:1 com:1 anne:1 activation:1 yet:1 diederik:1 must:4 pioneer:1 john:4 ronald:1 shape:27 enables:1 drop:1 interpretable:1 depict:1 ... |
6,906 | 7,247 | Learning Linear Dynamical Systems
via Spectral Filtering
Elad Hazan, Karan Singh, Cyril Zhang
Department of Computer Science
Princeton University
Princeton, NJ 08544
{ehazan,karans,cyril.zhang}@cs.princeton.edu
Abstract
We present an efficient and practical algorithm for the online prediction of
discrete-time linear ... | 7247 |@word version:1 polynomial:6 seems:1 norm:4 stronger:1 c0:2 kbkf:3 heuristically:1 km:3 simulation:2 crucially:1 pick:1 commute:1 thereby:1 tr:1 moment:1 initial:2 liu:1 series:8 contains:1 zij:2 ours:4 interestingly:2 reine:1 past:1 existing:2 outperforms:1 discretization:1 nt:1 tackling:1 must:3 john:1 numerica... |
6,907 | 7,248 | Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal
MILA, Universit? de Montr?al
Alessandro Sordoni
Microsoft Maluuba
Nan Rosemary Ke
MILA, Polytechnique Montr?al
Marc-Alexandre C?t?
Microsoft Maluuba
Yoshua Bengio
MILA, Universit? de Montr?al
Abstract
Many efforts have been devoted to training genera... | 7248 |@word multitask:1 version:3 proportion:1 nd:1 hu:4 bachman:7 pg:1 pressure:1 contains:1 selecting:1 ours:22 existing:1 current:3 activation:1 uria:2 concatenate:1 shape:2 drop:1 interpretable:2 update:3 plot:1 polyphonic:1 alone:4 generative:22 half:1 greedy:1 shut:1 intelligence:1 beginning:1 parametrization:1 s... |
6,908 | 7,249 | Learning Hierarchical Information Flow
with Recurrent Neural Modules
Danijar Hafner ?
Google Brain
mail@danijar.com
Alex Irpan
Google Brain
alexirpan@google.com
James Davidson
Google Brain
jcdavidson@google.com
Nicolas Heess
Google DeepMind
heess@google.com
Abstract
We propose ThalNet, a deep learning model inspire... | 7249 |@word multitask:2 compression:1 stronger:1 norm:2 seems:3 open:1 calculus:1 pick:1 mention:1 harder:1 recursively:1 initial:1 configuration:2 contains:1 paw:1 score:2 liu:1 groundwork:1 interestingly:1 reynolds:1 outperforms:4 favouring:1 existing:1 current:5 com:4 comparing:1 freitas:2 past:1 activation:1 must:1... |
6,909 | 725 | Implementing Intelligence on Silicon
Using Neuron-Like Functional MOS Transistors
Tadashi Shibata t Koji Kotani t Takeo Yamashita t Hiroshi Ishii
Hideo Kosaka t and Tadahiro Ohmi
Department of Electronic Engineering
Tohoku University
Aza-Aoba, Aramaki, Aobaku, Sendai 980 lAPAN
Abstract
We will present the implementati... | 725 |@word version:1 loading:1 calculus:1 pulse:5 simplifying:1 dramatic:3 reduction:4 initial:1 configuration:3 current:1 stemmed:1 follower:4 takeo:1 plasticity:2 v:1 intelligence:4 device:11 nervous:1 sram:2 leamed:1 short:1 alterable:3 math:1 location:1 firstly:1 c2:1 direct:1 constructed:1 become:1 differential:1 ... |
6,910 | 7,250 | Neural Variational Inference and Learning
in Undirected Graphical Models
Volodymyr Kuleshov
Stanford University
Stanford, CA 94305
kuleshov@cs.stanford.edu
Stefano Ermon
Stanford University
Stanford, CA 94305
ermon@cs.stanford.edu
Abstract
Many problems in machine learning are naturally expressed in the language of
... | 7250 |@word version:1 nd:1 confirms:1 seek:1 contrastive:4 sgd:1 reduction:3 initial:2 contains:2 score:4 jimenez:2 document:1 interestingly:5 diederik:3 dx:4 written:1 john:2 periodically:1 visible:4 partition:23 shape:1 enables:3 plot:3 generative:9 intelligence:2 scotland:1 smith:1 blei:5 provides:1 pascanu:1 org:3 ... |
6,911 | 7,251 | Subspace Clustering via Tangent Cones
Amin Jalali
Wisconsin Institute for Discovery
University of Wisconsin
Madison, WI 53715
amin.jalali@wisc.edu
Rebecca Willett
Department of Electrical and Computer Engineering
University of Wisconsin
Madison, WI 53706
willett@discovery.wisc.edu
Abstract
Given samples lying on any ... | 7251 |@word trial:5 illustrating:1 version:3 inversion:1 norm:1 open:4 shuicheng:1 seek:1 simulation:1 solid:3 harder:2 reduction:1 celebrated:1 configuration:3 mag:1 denoting:1 past:1 existing:3 recovered:1 current:4 nt:9 optim:1 si:1 assigning:1 must:1 takeo:1 mesh:1 csc:28 subsequent:1 numerical:1 plot:1 aside:1 gen... |
6,912 | 7,252 | The Neural Hawkes Process: A Neurally
Self-Modulating Multivariate Point Process
Hongyuan Mei
Jason Eisner
Department of Computer Science, Johns Hopkins University
3400 N. Charles Street, Baltimore, MD 21218 U.S.A
{hmei,jason}@cs.jhu.edu
Abstract
Many events occur in the world. Some event types are stochastically exci... | 7252 |@word luk:1 repository:1 version:2 seems:1 nd:1 extinction:1 rajaraman:1 d2:1 simulation:1 tried:1 excited:1 dramatic:1 mention:4 minus:1 solid:1 reduction:1 initial:1 liu:1 cellphone:1 series:2 contains:2 document:2 prefix:3 past:31 outperforms:2 current:2 wd:1 michal:1 comparing:2 manuel:2 com:2 yet:2 conjuncti... |
6,913 | 7,253 | Inverse Reward Design
Dylan Hadfield-Menell
Smitha Milli Pieter Abbeel? Stuart Russell Anca Dragan
Department of Electrical Engineering and Computer Science
University of California, Berkeley
Berkeley, CA 94709
{dhm, smilli, pabbeel, russell, anca}@cs.berkeley.edu
Abstract
Autonomous agents optimize the reward functi... | 7253 |@word h:1 middle:2 proportion:1 open:1 pieter:4 rgb:1 jacob:1 excited:1 dramatic:1 contains:1 selecting:2 unintended:1 daniel:1 outperforms:1 coactive:1 current:1 comparing:1 com:1 surprising:1 yet:1 must:4 john:3 realize:1 evans:2 sorg:2 subsequent:1 realistic:3 informative:1 shape:1 enables:2 ashesh:1 designed:... |
6,914 | 7,254 | Structured Bayesian Pruning via Log-Normal
Multiplicative Noise
Kirill Neklyudov 1,2
k.necludov@gmail.com
1
Dmitry Molchanov 1,3
dmolchanov@hse.ru
Arsenii Ashukha 1,2
Dmitry Vetrov 1,2
aashukha@hse.ru
National Research University Higher School of Economics
3
Skolkovo Institute of Science and Technology
dvetrov@h... | 7254 |@word kohli:1 version:1 compression:6 tried:1 decomposition:1 sparsifies:1 mention:1 ld:7 liu:1 contains:4 ours:6 document:1 existing:3 com:2 activation:3 gmail:1 diederik:1 gpu:4 devin:1 additive:1 shape:4 christian:1 remove:10 drop:4 podoprikhin:2 provides:9 pascanu:1 math:1 preference:1 firstly:1 zhang:3 rc:1 ... |
6,915 | 7,255 | Attend and Predict: Understanding Gene Regulation
by Selective Attention on Chromatin
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Department of Computer Science
University of Virginia
yanjun@virginia.edu
Abstract
The past decade has seen a revolution in genomic technologies that enabled a flood
of g... | 7255 |@word katja:1 repository:2 cnn:18 middle:3 diyi:1 integrative:1 seek:1 tried:2 accommodate:1 carry:1 contains:4 score:8 daniel:1 genetic:3 bc:2 document:3 interestingly:1 past:1 current:5 comparing:1 activation:1 k562:5 must:1 written:1 john:1 destiny:1 distant:1 hypothesize:1 designed:1 plot:4 interpretable:2 in... |
6,916 | 7,256 | Acceleration and Averaging
In Stochastic Descent Dynamics
Walid Krichene
Google, Inc.
walidk@google.com
Peter Bartlett
U.C. Berkeley
bartlett@cs.berkeley.edu
Abstract
We formulate and study a general family of (continuous-time) stochastic dynamics
for accelerated first-order minimization of smooth convex functions.
... | 7256 |@word middle:1 version:1 polynomial:2 instrumental:1 norm:4 johansson:1 guillin:1 open:2 d2:4 calculus:1 seek:1 nemirovsky:4 simplifying:1 covariance:1 contraction:1 tr:5 carry:1 reduction:3 initial:4 series:3 com:1 discretization:7 comparing:1 si:1 dx:2 written:1 update:1 juditsky:1 vanishing:6 hamiltonian:1 chi... |
6,917 | 7,257 | Kernel functions based on triplet comparisons
Matth?us Kleindessner?
Department of Computer Science
Rutgers University
Piscataway, NJ 08854
mk1572@cs.rutgers.edu
Ulrike von Luxburg
Department of Computer Science
University of T?bingen
Max Planck Institute for Intelligent Systems, T?bingen
luxburg@informatik.uni-tuebin... | 7257 |@word kulis:1 middle:1 version:1 seems:1 nd:4 tried:1 paid:1 minus:1 versatile:1 moment:1 liu:1 contains:4 score:18 zuk:1 series:1 document:1 existing:1 current:1 comparing:2 com:1 numerical:1 kdd:1 plot:12 designed:1 drop:1 implying:1 intelligence:2 selected:1 item:5 warmuth:1 inspection:1 xk:13 core:1 colored:1... |
6,918 | 7,258 | An Error Detection and Correction Framework for
Connectomics
Jonathan Zung
Princeton University
jzung@princeton.edu
Ignacio Tartavull
Princeton University
tartavull@princeton.edu
Kisuk Lee
Princeton University and MIT
kisuklee@mit.edu
H. Sebastian Seung
Princeton University
sseung@princeton.edu
Abstract
We define a... | 7258 |@word version:1 kokkinos:1 chakraborty:1 paredes:1 termination:1 mengye:1 tr:1 shot:1 briggman:3 reduction:1 initial:5 contains:2 score:3 exclusively:1 piotr:1 daniel:2 bootstrapped:2 romera:1 err:1 guadarrama:1 current:1 comparing:2 com:1 yet:1 dx:2 connectomics:4 gpu:2 grain:1 john:1 subsequent:2 ronan:1 inform... |
6,919 | 7,259 | Style Transfer from Non-Parallel Text by
Cross-Alignment
Tianxiao Shen1
Tao Lei2 Regina Barzilay1 Tommi Jaakkola1
2
MIT CSAIL
ASAPP Inc.
1
{tianxiao, regina, tommi}@csail.mit.edu 2 tao@asapp.com
1
Abstract
This paper focuses on style transfer on the basis of non-parallel text. This is an
instance of a broad family o... | 7259 |@word version:1 eliminating:1 seems:1 logit:1 nonsensical:1 bf:1 open:2 cha:1 hu:11 d2:8 pieter:1 jacob:1 pg:2 reap:1 schmaltz:2 thereby:1 harder:3 carry:2 initial:4 substitution:18 liu:5 score:7 ndez:2 contains:1 lantao:1 document:2 interestingly:1 hyunsoo:1 outperforms:1 recovered:4 com:2 z2:5 surprising:1 comp... |
6,920 | 726 | On the Non-Existence of a Universal Learning
Algorithm for Recurrent Neural Networks
Herbert Wiklicky
Centrum voor Wiskunde en Informatica
P.O.Box 4079, NL-1009 AB Amsterdam, The Netherlands?
e-mail: herbert@cwi.nl
Abstract
We prove that the so called "loading problem" for (recurrent) neural networks is unsolvable. T... | 726 |@word unaltered:1 polynomial:2 loading:11 seems:1 moment:1 phy:1 configuration:4 initial:1 activation:2 ronald:1 drop:1 update:2 rrt:1 intelligence:1 completeness:1 math:2 node:2 mathematical:3 constructed:4 become:1 symposium:1 learing:2 prove:2 vitter:3 indeed:1 hardness:1 behavior:6 freeman:1 little:1 consideri... |
6,921 | 7,260 | Cross-Spectral Factor Analysis
Neil M. Gallagher*,1 , Kyle Ulrich*,2 , Austin Talbot3 ,
Kafui Dzirasa1,4 , Lawrence Carin2 and David E. Carlson5,6
1
Department of Neurobiology, 2 Department of Electrical and Computer Engineering, 3 Department of
Statistical Science, 4 Department of Psychiatry and Behavioral Sciences, ... | 7260 |@word multitask:1 pw:1 manageable:1 hippocampus:2 open:6 covariance:12 accounting:1 simplifying:2 concise:1 carry:1 deisseroth:2 reduction:3 score:18 united:1 genetic:3 current:3 comparing:1 trustworthy:1 activation:1 additive:3 eleven:1 analytic:1 designed:2 interpretable:7 medial:2 plot:5 stationary:4 generativ... |
6,922 | 7,261 | Stochastic Submodular Maximization:
The Case of Coverage Functions
Mohammad Reza Karimi
Department of Computer Science
ETH Zurich
mkarimi@ethz.ch
Mario Lucic
Department of Computer Science
ETH Zurich
lucic@inf.ethz.ch
Hamed Hassani
Department of Electrical and Systems Engineering
University of Pennsylvania
hassani@se... | 7261 |@word polynomial:1 achievable:1 norm:4 nd:1 laurence:2 seek:1 simulation:1 bn:1 sgd:6 biconjugate:1 contains:1 daniel:2 ours:1 document:1 outperforms:4 diederik:1 john:1 partition:6 wx:3 seeding:1 designed:1 greedy:4 selected:4 prohibitive:1 item:1 instantiate:1 intelligence:5 advancement:1 provides:2 node:12 loc... |
6,923 | 7,262 | Affinity Clustering: Hierarchical Clustering at Scale
MohammadHossein Bateni
Google Research
bateni@google.com
MohammadTaghi Hajiaghayi?
University of Maryland
hajiagha@cs.umd.edu
Soheil Behnezhad?
University of Maryland
soheil@cs.umd.edu
Raimondas Kiveris
Google Research
rkiveris@google.com
Mahsa Derakhshan?
Univer... | 7262 |@word private:1 repository:1 polynomial:1 seems:1 nd:3 disk:6 rajaraman:1 confirms:1 simulation:1 hsieh:1 jacob:1 pick:2 bahmani:1 liu:1 contains:2 score:6 united:1 lichman:1 silviol:1 franklin:1 past:2 outperforms:1 steiner:4 imaginary:1 com:5 surprising:1 goldberger:1 must:1 sergei:4 john:1 mst:33 porta:1 happe... |
6,924 | 7,263 | Unsupervised Transformation Learning
via Convex Relaxations
Tatsunori B. Hashimoto John C. Duchi Percy Liang
Stanford University
Stanford, CA 94305
{thashim,jduchi,pliang}@cs.stanford.edu
Abstract
Our goal is to extract meaningful transformations from raw images, such as varying
the thickness of lines in handwriting o... | 7263 |@word version:3 inversion:1 norm:11 seek:3 covariance:1 tr:3 moment:1 reduction:1 contains:2 ours:1 interestingly:1 past:2 existing:2 outperforms:1 current:1 ka:2 recovered:1 activation:1 si:1 written:2 must:1 john:1 distant:1 blur:2 extrapolating:1 interpretable:1 unidentifiability:1 implying:1 generative:1 alon... |
6,925 | 7,264 | A Sharp Error Analysis for the Fused Lasso, with
Application to Approximate Changepoint Screening
Kevin Lin
Carnegie Mellon University
Pittsburgh, PA 15213
kevinl1@andrew.cmu.edu
James Sharpnack
University of California, Davis
Davis, CA 95616
jsharpna@ucdavis.edu
Alessandro Rinaldo
Carnegie Mellon University
Pittsbu... | 7264 |@word worsens:1 version:1 polynomial:1 norm:1 seems:1 stronger:1 simulation:1 bn:37 decomposition:2 p0:4 boundedness:5 series:3 genetic:1 document:1 ours:1 denoting:1 existing:1 current:2 comparing:3 written:1 must:1 boysen:2 stemming:1 kdb:1 numerical:1 john:1 remove:1 stationary:1 greedy:2 rudin:2 inspection:1 ... |
6,926 | 7,265 | Linear Time Computation of Moments in
Sum-Product Networks
Geoff Gordon
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Han Zhao
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
han.zhao@cs.cmu.edu
Abstract
Bayesian online algorithms for Sum-Pr... | 7265 |@word version:1 polynomial:18 twelfth:1 jointree:1 bn:6 p0:21 recursively:1 reduction:6 moment:61 liu:1 score:1 outperforms:1 existing:2 must:1 attracted:1 written:1 realize:2 partition:1 designed:2 update:13 intelligence:6 leaf:9 greedy:1 realizing:2 record:2 node:45 revisited:1 height:2 along:1 constructed:3 di... |
6,927 | 7,266 | A Meta-Learning Perspective on Cold-Start
Recommendations for Items
Manasi Vartak?
Massachusetts Institute of Technology
mvartak@csail.mit.edu
Jeshua Bratman
Twitter Inc.
jbratman@twitter.com
Arvind Thiagarajan
Twitter Inc.
arvindt@twitter.com
Conrado Miranda
Twitter Inc.
cmiranda@twitter.com
Hugo Larochelle?
Google... | 7266 |@word version:2 proportion:1 replicate:1 nd:1 cleanly:1 seek:2 bachman:1 sgd:1 rj0:6 shot:7 shading:2 liu:2 contains:1 score:1 selecting:2 t7:2 tuned:2 ours:1 document:1 past:3 outperforms:2 duong:1 current:6 com:7 activation:1 must:7 reminiscent:1 readily:2 periodically:1 enables:1 drop:1 update:1 intelligence:2... |
6,928 | 7,267 | Predicting Scene Parsing and Motion Dynamics
in the Future
Xiaojie Jin1 , Huaxin Xiao2 , Xiaohui Shen3 , Jimei Yang3 , Zhe Lin3
Yunpeng Chen2 , Zequn Jie4 , Jiashi Feng2 , Shuicheng Yan5,2
1
2
NUS Graduate School for Integrative Science and Engineering (NGS), NUS
Department of ECE, NUS 3 Adobe Research 4 Tencent AI L... | 7267 |@word cnn:3 cox:1 kokkinos:1 bptt:1 open:1 integrative:1 shuicheng:2 jacob:2 inpainting:2 initial:1 configuration:1 contains:3 liu:1 daniel:2 bppt:1 ours:8 existing:2 current:1 com:1 luo:2 parsing:97 subsequent:1 happen:1 shape:2 enables:1 zaid:1 treating:1 update:1 standalone:1 intelligence:2 generative:3 accord... |
6,929 | 7,268 | Sticking the Landing: Simple, Lower-Variance
Gradient Estimators for Variational Inference
Geoffrey Roeder
University of Toronto
roeder@cs.toronto.edu
Yuhuai Wu
University of Toronto
ywu@cs.toronto.edu
David Duvenaud
University of Toronto
duvenaud@cs.toronto.edu
Abstract
We propose a simple and general variant of th... | 7268 |@word briefly:1 eliminating:2 norm:1 nd:1 scalably:1 covariance:1 moment:1 reduction:8 initial:1 contains:1 score:23 efficacy:1 jimenez:4 ours:1 interestingly:2 outperforms:3 existing:5 steiner:1 current:1 com:3 mari:1 diederik:3 written:1 must:1 exposing:1 gpu:1 devin:1 ronan:1 ronald:1 informative:1 analytic:2 ... |
6,930 | 7,269 | Efficient Approximation Algorithms for String Kernel
Based Sequence Classification
Muhammad Farhan
Department of Computer Science
School of Science and Engineering
Lahore University of Management Sciences
Lahore, Pakistan
14030031@lums.edu.pk
Juvaria Tariq
Department of Mathematics
School of Science and Engineering
L... | 7269 |@word polynomial:1 nd:1 mers:20 open:2 decomposition:1 elisseeff:2 nystr:3 reduction:1 bai:1 mudassir:2 substitution:1 score:5 selecting:1 mi0:12 document:1 ullah:1 existing:3 emory:1 com:1 readily:1 cruz:1 additive:2 visible:1 enables:1 plot:1 v:1 prohibitive:1 leaf:1 nq:4 selected:1 cook:1 core:1 eskin:2 detect... |
6,931 | 727 | VLSI Phase Locking Architectures for
Feature Linking in Multiple Target
Tracking Systems
Andreas G. Andreou
andreou@jhunix.hcf.jhu.edu
Department of Electrical and
Computer Engineering
The Johns Hopkins University
Baltimore, MD 21218
Thomas G. Edwards
tedwards@src.umd.edu
Department of Electrical Engineering
The Univ... | 727 |@word middle:1 seems:1 simulation:5 pulse:9 brightness:1 current:4 yet:1 follower:3 must:1 readily:1 john:1 j1:3 designed:1 plot:2 v:2 discrimination:1 device:1 sys:1 provides:1 location:4 c2:1 become:2 consists:1 resistive:3 baldi:2 olfactory:2 inter:1 rapid:1 oscilloscope:1 simulator:1 brain:1 freeman:2 little:3... |
6,932 | 7,270 | Kernel Feature Selection via
Conditional Covariance Minimization
Jianbo Chen?
University of California, Berkeley
jianbochen@berkeley.edu
Martin J. Wainwright
University of California, Berkeley
wainwrig@berkeley.edu
Mitchell Stern?
University of California, Berkeley
mitchell@berkeley.edu
Michael I. Jordan
University of... | 7270 |@word trial:1 repository:2 version:2 achievable:1 polynomial:1 norm:1 adnan:1 covariance:18 decomposition:1 elisseeff:2 tr:6 carry:2 reduction:6 initial:3 configuration:1 series:1 liu:1 selecting:2 lichman:1 wrapper:5 denoting:2 rkhs:6 dubourg:1 past:2 wainwrig:1 existing:1 outperforms:2 bradley:1 written:1 john:... |
6,933 | 7,271 | Convergence of Gradient EM on Multi-component
Mixture of Gaussians
Bowei Yan
University of Texas at Austin
boweiy@utexas.edu
Mingzhang Yin
University of Texas at Austin
mzyin@utexas.edu
Purnamrita Sarkar
University of Texas at Austin
purna.sarkar@austin.utexas.edu
Abstract
In this paper, we study convergence propert... | 7271 |@word trial:1 version:3 polynomial:1 proportion:3 norm:4 c0:1 sex:1 unif:6 simulation:1 contraction:30 covariance:5 sheffet:1 mention:1 accommodate:1 carry:1 series:2 daniel:2 renewed:1 mixon:1 existing:1 current:1 comparing:1 numerical:2 confirming:1 drop:1 plot:3 update:7 stationary:5 intelligence:1 fewer:1 iso... |
6,934 | 7,272 | Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski
pd437@cam.ac.uk
University of Cambridge
Yarin Gal
yarin.gal@eng.cam.ac.uk
University of Cambridge
and Alan Turing Institute, London
Abstract
In this work we develop a fast saliency detection method that can be applied to
any differentiable image class... | 7272 |@word cnn:1 version:2 stronger:2 seems:1 seal:4 c0:3 nd:1 confirms:1 eng:1 tr:1 harder:3 carry:1 initial:1 liu:2 contains:6 score:6 groundwork:1 tuned:1 interestingly:1 outperforms:5 existing:2 err:2 current:1 surprising:1 activation:2 dx:1 must:2 gpu:1 intriguing:1 visible:1 subsequent:2 informative:1 blur:4 con... |
6,935 | 7,273 | Houdini: Fooling Deep Structured Visual and Speech
Recognition Models with Adversarial Examples
Moustapha Cisse
Facebook AI Research
moustaphacisse@fb.com
Yossi Adi*
Bar-Ilan University, Israel
yossiadidrum@gmail.com
Natalia Neverova*
Facebook AI Research
nneverova@fb.com
Joseph Keshet
Bar-Ilan University, Israel
j... | 7273 |@word moosavi:2 cnn:1 version:4 norm:4 proportion:1 underline:1 seems:1 valle:1 termination:1 rgb:1 harder:1 initial:2 substitution:1 series:1 score:8 ndez:1 denoting:2 panayotov:1 animated:2 existing:2 com:3 transferability:1 gmail:1 yet:1 must:3 intriguing:1 fn:2 christian:1 visibility:1 designed:4 hourglass:2 ... |
6,936 | 7,274 | Efficient and Flexible Inference for Stochastic
Systems
Stefan Bauer?
Department of Computer Science
ETH Zurich
bauers@inf.ethz.ch
Nico S. Gorbach?
Department of Computer Science
ETH Zurich
ngorbach@inf.ethz.ch
?orde
? Miladinovi?c
Department of Computer Science
ETH Zurich
djordjem@inf.ethz.ch
Joachim M. Buhmann
Dep... | 7274 |@word polynomial:2 stronger:1 c0:1 calculus:5 closure:1 simulation:1 linearized:1 covariance:4 initial:1 offering:2 denoting:1 outperforms:1 current:3 od:1 yet:1 dx:20 written:2 gpu:2 john:1 realistic:1 numerical:8 additive:4 sdes:6 remove:2 plot:3 designed:1 v:1 stationary:2 xk:14 manfred:3 simpler:1 mathematica... |
6,937 | 7,275 | When Cyclic Coordinate Descent Outperforms
Randomized Coordinate Descent
Mert G?rb?zbalaban?, Asuman Ozdaglar?, Pablo A. Parrilo?, N. Denizcan Vanli?
?
Rutgers University, mg1366@rutgers.edu
?
Massachusetts Institute of Technology, {asuman,parrilo,denizcan}@mit.edu
Abstract
The coordinate descent (CD) method is a clas... | 7275 |@word mild:1 version:2 norm:2 stronger:1 nd:1 hu:3 decomposition:3 pick:1 reduction:1 initial:3 cyclic:35 contains:1 outperforms:1 luo:2 si:1 yet:1 written:2 numerical:7 plot:1 update:6 selected:2 xk:3 ith:3 reciprocal:1 isotone:1 characterization:4 iterates:5 zbalaban:1 nussbaum:1 mathematical:4 along:2 direct:1... |
6,938 | 7,276 | Active Learning from Peers
Keerthiram Murugesan
Jaime Carbonell
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{kmuruges,jgc}@cs.cmu.edu
Abstract
This paper addresses the challenge of learning from peers in an online multitask
setting. Instead of always requesting a label from a human orac... | 7276 |@word multitask:23 version:1 middle:5 norm:1 dekel:3 km:16 jacob:1 incurs:1 thereby:1 keerthiram:2 kwm:1 moment:1 venkatasubramanian:1 liu:1 initial:2 score:1 selecting:1 tuned:1 outperforms:2 existing:2 current:11 com:1 attracted:1 readily:1 john:1 drop:1 plot:2 update:6 v:2 intelligence:1 rts:1 xk:2 ith:1 provi... |
6,939 | 7,277 | Experimental Design for Learning Causal Graphs
with Latent Variables
Murat Kocaoglu?
Department of Electrical and Computer Engineering
The University of Texas at Austin, USA
mkocaoglu@utexas.edu
Karthikeyan Shanmugam?
IBM Research NY, USA
karthikeyan.shanmugam2@ibm.com
Elias Bareinboim
Department of Computer Science ... | 7277 |@word version:3 stronger:1 nd:1 open:1 d2:9 hu:1 closure:15 covariance:1 maes:1 tr:9 reduction:17 initial:1 cyclic:1 series:2 exclusively:1 hereafter:1 contains:2 daniel:1 freitas:1 recovered:1 com:1 nicolai:2 si:3 attracted:1 additive:1 partition:1 shape:1 mackey:1 intelligence:5 greedy:3 discovering:1 dun:1 xk:... |
6,940 | 7,278 | Learning to Model the Tail
Yu-Xiong Wang
Deva Ramanan
Martial Hebert
Robotics Institute, Carnegie Mellon University
{yuxiongw,dramanan, hebert}@cs.cmu.edu
Abstract
We describe an approach to learning from long-tailed, imbalanced datasets that
are prevalent in real-world settings. Here, the challenge is to learn accur... | 7278 |@word multitask:1 cnn:13 version:3 norm:4 everingham:1 open:1 underperform:1 gradual:2 bn:3 sgd:4 solid:1 shot:87 recursively:1 reduction:1 initial:1 liu:2 series:1 contains:2 hoiem:1 tuned:7 ours:12 interestingly:2 past:2 outperforms:4 existing:1 current:3 freitas:1 guadarrama:1 activation:2 yet:1 subsequent:2 d... |
6,941 | 7,279 | Stochastic Mirror Descent in
Variationally Coherent Optimization Problems
Zhengyuan Zhou
Stanford University
zyzhou@stanford.edu
Nicholas Bambos
Stanford University
bambos@stanford.edu
Panayotis Mertikopoulos
Univ. Grenoble Alpes, CNRS, Inria, LIG
panayotis.mertikopoulos@imag.fr
Stephen Boyd
Stanford University
boyd@... | 7279 |@word briefly:1 norm:3 stronger:1 heuristically:1 simulation:3 mention:2 thereby:2 harder:1 carry:1 moment:1 initial:6 contains:3 exclusively:1 score:3 series:2 existing:1 current:1 must:1 written:1 fn:1 plot:1 update:1 device:1 affair:1 short:2 characterization:2 iterates:12 provides:1 successive:1 org:1 mathema... |
6,942 | 728 | Bayesian Backpropagation Over 1-0 Functions
Rather Than Weights
David H. Wolpert
The Santa Fe Institute
1660 Old Pecos Trail
Santa Fe, NM 87501
Abstract
The conventional Bayesian justification of backprop is that it finds the
MAP weight vector. As this paper shows, to find the MAP i-o function
instead one must add a c... | 728 |@word determinant:1 version:1 pw:16 seems:1 simplifying:1 tr:4 pub:1 nowlan:4 yet:1 must:8 remove:2 aside:2 alone:2 tenn:7 v:1 device:1 guess:1 selected:2 accordingly:5 fewer:1 compo:1 characterization:1 location:1 successive:1 ofbackpropagation:1 ironically:1 redefine:1 introduce:1 multi:2 brain:1 automatically:1... |
6,943 | 7,280 | On Separability of Loss Functions, and Revisiting
Discriminative Vs Generative Models
Adarsh Prasad
Machine Learning Dept.
CMU
adarshp@andrew.cmu.edu
Alexandru Niculescu-Mizil
NEC Laboratories America
Princeton, NJ, USA
alex@nec-labs.com
Pradeep Ravikumar
Machine Learning Dept.
CMU
pradeepr@cs.cmu.edu
Abstract
We re... | 7280 |@word mild:1 trial:1 determinant:1 version:2 norm:7 d2:3 simulation:1 prasad:2 covariance:3 contraction:1 harder:2 liu:1 ours:2 xinyang:1 com:1 z2:1 comparing:4 attracted:1 written:7 john:2 partition:1 plot:1 v:5 generative:73 instantiate:4 fewer:1 intelligence:1 accordingly:1 isotropic:15 xk:2 volkan:1 firstly:1... |
6,944 | 7,281 | Maxing and Ranking with Few Assumptions
Moein Falahatgar Yi Hao Alon Orlitsky Venkatadheeraj Pichapati Vaishakh Ravindrakumar
University of California, San Deigo
{moein,yih179,alon,dheerajpv7,vaishakhr}@ucsd.edu
Abstract
1
1.1
PAC maximum selection (maxing) and ranking of n elements via random pairwise
comparisons h... | 7281 |@word trial:1 version:2 advantageous:1 seek:1 tried:2 atul:1 fabrice:1 deems:2 pick:6 jafarpour:3 reduction:5 contains:8 score:14 document:1 animated:1 trueskill:2 whp:5 comparing:1 yet:3 must:3 evans:1 realistic:2 additive:1 drop:1 designed:1 update:3 alone:1 half:1 selected:1 website:1 fewer:2 intelligence:1 sh... |
6,945 | 7,282 | On clustering network-valued data
Soumendu Sundar Mukherjee
Department of Statistics
University of California, Berkeley
Berkeley, California 94720, USA
soumendu@berkeley.edu
Purnamrita Sarkar
Department of Statistics and Data Sciences
University of Texas, Austin
Austin, Texas 78712, USA
purna.sarkar@austin.utexas.edu... | 7282 |@word mild:1 kolaczyk:1 version:1 briefly:1 kondor:1 proportion:2 norm:2 nd:4 suitably:2 open:1 d2:1 km:1 simulation:5 tried:1 p0:1 citeseer:6 q1:1 mention:1 minus:1 moment:15 contains:1 efficacy:1 tuned:2 past:2 existing:1 outperforms:1 current:1 comparing:5 com:1 yet:1 universality:1 partition:1 informative:1 c... |
6,946 | 7,283 | A General Framework for Robust Interactive
Learning?
Ehsan Emamjomeh-Zadeh?
David Kempe?
Abstract
We propose a general framework for interactively learning models, such as (binary
or non-binary) classifiers, orderings/rankings of items, or clusterings of data points.
Our framework is based on a generalization of Ang... | 7283 |@word version:5 polynomial:4 nd:4 open:3 bn:1 pick:1 reduction:1 initial:1 contains:7 series:1 interestingly:1 comparing:1 luo:1 yet:1 must:14 readily:1 reminiscent:1 realistic:1 partition:3 informative:2 update:1 n0:9 fewer:1 selected:1 item:13 ith:1 transposition:4 provides:5 node:29 preference:4 hyperplanes:1 ... |
6,947 | 7,284 | Multi-view Matrix Factorization for Linear
Dynamical System Estimation
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesv?ri
Department of Computer Science
University of Alberta
Edmonton, AB, Canada
{karami1, whitem, daes, szepesva}@ualberta.ca
Abstract
We consider maximum likelihood estimation of linear dyna... | 7284 |@word version:1 loading:3 norm:7 nd:1 c0:1 open:1 decomposition:1 covariance:3 tr:2 reduction:3 moment:5 series:14 selecting:1 unintended:1 tuned:1 outperforms:3 atlantic:1 recovered:1 comparing:1 scatter:1 must:1 readily:1 enables:2 analytic:1 designed:3 interpretable:1 update:5 kv1:5 plot:1 generative:2 selecte... |
6,948 | 729 | Efficient Simulation of Biological Neural
Networks on Massively Parallel
Supercomputers with Hypercube
Archi tect ure
Ernst Niebur
Computation and Neural Systems
California Institute of Technology
Pasadena, CA 91125, USA
Dean Brettle
Booz, Allen and Hamilton, Inc.
8283 Greensboro Drive
McLean, VA 22102-3838, USA
Abs... | 729 |@word simulation:9 crucially:1 efficacy:1 optican:2 current:1 numerical:1 realistic:3 partition:9 isotropic:1 short:1 zhang:2 along:1 direct:6 overhead:5 inter:1 expected:2 behavior:1 growing:1 simulator:6 touchstone:1 increasing:1 provided:2 cm:3 monkey:1 impractical:1 temporal:4 every:2 ti:1 exactly:1 unit:1 ham... |
6,949 | 73 | 211
HIGH DENSITY ASSOCIATIVE MEMORIES!
A"'ir Dembo
Information Systems Laboratory, Stanford University
Stanford, CA 94305
Ofer Zeitouni
Laboratory for Information and Decision Systems
MIT, Cambridge, MA 02139
ABSTRACT
A class of high dens ity assoc iat ive memories is constructed,
starting from a description of desir... | 73 |@word version:3 q1:1 eld:1 minus:1 carry:1 initial:1 medi:1 emory:1 si:1 yet:1 attracted:1 shape:1 enables:1 ints:1 update:1 stationary:2 spec:1 dembo:4 ial:3 short:2 hypersphere:6 characterization:1 location:2 simpler:1 along:1 constructed:2 inside:4 behavior:1 decreasing:1 provided:3 bounded:1 moreover:2 circuit:... |
6,950 | 730 | Optimal signalling in Attractor Neural
Networks
Isaac Meilijson
Eytan Ruppin . .
School of Mathematical Sciences
Raymond and Beverly Sackler Faculty of Exact Sciences
Tel-Aviv University, 69978 Tel-Aviv, Israel.
Abstract
In [Meilijson and Ruppin, 1993] we presented a methodological
framework describing the two-iterati... | 730 |@word version:4 faculty:1 achievable:1 seems:1 simulation:2 initial:10 tuned:1 nonmonotone:3 current:2 discretization:1 activation:3 reminiscent:1 slanted:5 numerical:1 additive:1 shape:3 enables:1 plot:1 half:1 signalling:13 preference:1 sigmoidal:3 mathematical:1 become:2 indeed:2 behavior:2 examine:1 multi:1 di... |
6,951 | 731 | High Performance Neural Net Simulation
on a Multiprocessor System with
"Intelligent" Communication
Urs A. Miiller, Michael Kocheisen, and Anton Gunzinger
Electronics Laboratory, Swiss Federal Institute of Technology
CH-B092 Zurich, Switzerland
Abstract
The performance requirements in experimental research on artifici... | 731 |@word cnn:3 loading:1 seems:1 replicate:1 disk:1 nd:1 instruction:3 simulation:15 propagate:3 carry:1 electronics:1 contains:2 series:2 existing:2 atlantic:1 written:1 john:1 evans:1 update:5 obsolete:1 device:1 ivo:1 accordingly:1 short:1 pointer:1 chua:2 idi:1 location:1 simpler:5 zhang:1 direct:1 cray:1 combine... |
6,952 | 733 | What Does the Hippocampus Compute?:
A Precis of the 1993 NIPS Workshop
Mark A. Gluck
Center for Molecular and Behavioral Neuroscience
Rutgers University
Newark, NJ 07102
gluck@pavlov.rutgers.edu
Computational models of the hippocampal-region provide an important method for
understanding the functional role of this bra... | 733 |@word classical:1 compression:1 hippocampus:9 seeking:1 direction:3 anatomy:1 gradual:1 human:4 packet:1 enable:1 runaway:1 self:1 recurrence:1 exhibit:1 noted:1 larson:1 simulated:1 rat:2 initial:2 capacity:1 hippocampal:15 sci:1 landmark:1 subtypes:1 extension:1 talked:1 hawkins:1 novel:2 piriform:1 predict:1 de... |
6,953 | 734 | Stability and Observability
Max Garzon
Fernanda Botelho
garzonmGhermea.maci.memat.edu botelhofGhermea.maci.memat.edu
Institute for Intelligent Systems Department of Mathematical Sciences
Memphis State University
Memphis, TN 38152 U.S.A.
The theme was the effect of perturbations of the defining parameters of a neural... | 734 |@word effect:4 requiring:1 involves:1 indicate:1 true:1 seems:2 quantify:1 exhibiting:1 question:2 open:1 symmetric:1 peterfreund:2 simulation:2 stochastic:1 exhibit:1 berlin:1 really:1 extent:1 biological:3 barely:1 tn:1 toward:1 bring:1 neuneier:1 com:1 around:1 code:1 index:1 relationship:1 equilibrium:3 diffic... |
6,954 | 735 | Unsupervised Learning of Mixtures of
Multiple Causes in Binary Data
Eric Saund
Xerox Palo Alto Research Center
3333 Coyote Hill Rd., Palo Alto, CA, 94304
Abstract
This paper presents a formulation for unsupervised learning of clusters reflecting multiple causal structure in binary data. Unlike the
standard mixture mo... | 735 |@word middle:2 duda:2 grey:4 seek:1 accounting:1 decomposition:1 pressure:2 shading:2 initial:3 iple:1 nt:1 nowlan:1 must:1 numerical:2 designed:1 five:3 mathematical:1 become:1 incorrect:1 combine:1 indeed:1 little:1 discover:1 underlying:3 alto:2 suite:2 sensibly:1 control:2 unit:6 causally:1 local:1 consequence... |
6,955 | 736 | Synchronization, oscillations, and 1/ f
noise in networks of spiking neurons
Martin Stemmler, Marius Usher, and Christof Koch
Computation and Neural Systems, 139-74
California Institute of Technology
Pasadena, CA 91125
Zeev Olami
Dept. of Chemical Physics
Weizmann Institute of Science
Rehovot 76100, Israel
Abstract
W... | 736 |@word neurophysiology:1 trial:1 cox:3 stronger:1 pulse:2 simulation:4 teich:6 eng:1 cyclic:1 series:1 interestingly:1 current:6 com:1 must:1 john:1 visible:1 latt:1 interspike:4 nervous:1 short:2 rc:1 mandelbrot:3 retrieving:1 consists:2 excitatorily:1 autocorrelation:1 olfactory:1 roughly:1 disrupts:1 frequently:... |
6,956 | 737 | The Statistical Mechanics of
k-Satisfaction
Scott Kirkpatrick*
Racah Institute for Physics and
Center for Neural Computation
Hebrew University
Jerusalem, 91904 Israel
kirk@fiz.huji.ac .il
Geza Gyorgyi
Institute for Theoretical Physics
Eotvos University
1-1088 Puskin u. 5-7
Budapest, Hungary
gyorgyi@ludens.elte.hu,
N ... | 737 |@word polynomial:1 sharpens:1 hu:1 accounting:1 tr:2 configuration:6 contains:1 att:1 tabulate:1 subjective:1 reaction:1 com:1 yet:1 conjunctive:1 aft:1 must:1 written:1 numerical:1 partition:1 shape:1 plot:1 v:1 metabolism:1 item:1 vanishing:1 compo:1 provides:1 completeness:3 characterization:1 math:2 attack:1 h... |
6,957 | 738 | Feature Densities are Required for
Computing Feature Correspondences
Subutai Ahmad
Interval Research Corporation
1801-C Page Mill Road, Palo Alto, CA 94304
E-mail: ahmadCDinterval.com
Abstract
The feature correspondence problem is a classic hurdle in visual
object-recognition concerned with determining the correct ma... | 738 |@word version:1 bf:3 tried:2 covariance:1 wiggling:1 pick:1 solid:1 configuration:1 score:1 selecting:5 outperforms:1 current:2 com:1 nowlan:2 written:1 must:1 subsequent:1 hofmann:1 plot:3 v:1 intelligence:1 selected:5 location:1 five:1 constructed:2 symposium:1 edelman:3 consists:1 incorrect:1 expected:1 nor:1 e... |
6,958 | 739 | Counting function theorem for
multi-layer networks
Adam Kowalczyk
Telecom Australia, Research Laboratories
770 Blackburn Road, Clayton, Vic. 3168, Australia
(a.kowalczyk@trl.oz.au)
Abstract
x
We show that a randomly selected N-tuple of points ofRn with
probability> 0 is such that any multi-layer percept ron with th... | 739 |@word determinant:1 version:1 polynomial:1 open:4 contains:1 exclusively:1 comparing:2 dx:1 must:2 bd:1 realistic:1 drop:1 v:1 half:1 selected:2 warmuth:1 complication:1 ron:1 hyperplanes:1 unbounded:1 along:1 director:1 axn:1 multi:7 decomposed:1 researched:1 provided:1 notation:1 circuit:1 kaufman:1 developed:1 ... |
6,959 | 74 | 760
A NOVEL NET THAT LEARNS
SEQUENTIAL DECISION PROCESS
G.Z. SUN, Y.C. LEE and H.H. CHEN
Department of PhYJicJ and AJtronomy
and
InJtitute for Advanced Computer StudieJ
UNIVERSITY OF MARYLAND,COLLEGE PARK,MD 20742
ABSTRACT
We propose a new scheme to construct neural networks to classify patterns. The new scheme has ... | 74 |@word judgement:1 seek:2 n8:1 initial:1 envision:1 puri:1 current:1 si:2 john:2 belmont:1 subsequent:1 numerical:1 visible:1 partition:1 update:2 leaf:3 node:21 ron:2 firstly:1 constructed:1 become:1 expected:1 multi:5 automatically:2 actual:1 totally:1 becomes:2 kind:1 truely:2 nj:2 every:1 nf:1 classifier:1 unit:... |
6,960 | 740 | Estimating analogical similarity by dot-products
of Holographic Reduced Representations.
Tony A. Plate
Department of Computer Science, University of Toronto
Toronto, Ontario, Canada M5S 1A4
email: tap@ai.utoronto.ca
Abstract
Models of analog retrieval require a computationally cheap method of
estimating similarity be... | 740 |@word middle:1 proportion:1 holyoak:2 wisniewski:2 score:12 etn:1 existing:1 z2:1 activation:1 conjunctive:3 must:7 john:10 ctyp:4 shape:1 cheap:2 designed:1 alone:1 intelligence:4 selected:1 item:6 provides:1 node:1 toronto:2 location:2 firstly:1 downing:1 along:1 constructed:4 become:1 manner:1 ol:1 little:1 con... |
6,961 | 741 | Identifying Fault-Prone Software
Modules Using Feed-Forward Networks:
A Case Study
N. Karunanithi
Room 2E-378, Bellcore
435 South Street
Morristown, NJ 07960
E-mail: karun@faline.bellcore.com
Abstract
Functional complexity of a software module can be measured in
terms of static complexity metrics of the program text... | 741 |@word trial:1 version:2 seems:1 nd:1 eng:7 paulsen:1 reduction:3 initial:2 contains:1 score:1 selecting:1 efficacy:1 existing:1 com:1 written:1 subsequent:1 numerical:1 remove:1 half:4 selected:2 provides:1 sigmoidal:3 mathematical:1 constructed:6 symp:1 mask:1 alspector:1 manager:2 decreasing:1 actual:1 encouragi... |
6,962 | 742 | The Parti-game Algorithm for Variable
Resolution Reinforcement Learning in
Multidimensional State-spaces
Andrew W. Moore
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213
Abstract
Parti-game is a new algorithm for learning from delayed rewards
in high dimensional real-valued state-spaces. In ... | 742 |@word trial:14 rising:1 coarseness:1 harder:2 recursively:1 initial:3 configuration:2 series:1 score:1 uncovered:1 past:1 current:2 must:7 partition:29 shape:1 remove:2 update:1 greedy:7 short:1 record:2 provides:1 coarse:1 node:1 mathematical:1 along:3 become:1 inside:2 manner:1 expected:2 roughly:1 themselves:1 ... |
6,963 | 743 | An Analog VLSI Model of Central Pattern
Generation in the Leech
Micah S. Siegel*
Department of Electrical Engineering
Yale University
New Haven, CT 06520
Abstract
I detail the design and construction of an analog VLSI model of the
neural system responsible for swimming behaviors of the leech. Why
the leech? The biolog... | 743 |@word dekker:1 contraction:1 fonn:1 current:3 anterior:4 realistic:2 designed:1 device:1 nervous:8 provides:1 location:1 successive:6 neuromimes:3 cpg:4 along:1 burst:2 constructed:1 behavioral:1 behavior:10 oscilloscope:1 aliasing:1 inspired:1 ote:1 encouraging:1 vertebrate:1 circuit:7 tic:1 evolved:1 selverston:... |
6,964 | 744 | Pulling It All Together: Methods for
Combining Neural Networks
Michael P. Perrone
Institute for Brain and Neural Systems
Brown University
Providence, RI
mpp@cns. brown. edu
The past several years have seen a tremendous growth in the complexity of the
recognition, estimation and control tasks expected of neural networ... | 744 |@word collinearity:1 brown:5 contact:1 norm:1 leibler:1 norma:1 fa:1 simulation:1 usual:1 decomposition:2 jacob:1 human:1 ogi:1 please:1 tr:1 hillsdale:1 backprop:1 argued:1 razor:1 reduction:1 noted:1 series:1 generalization:1 past:1 current:1 index:2 nowlan:2 consideration:1 jack:1 must:1 fe:1 robert:1 negative:... |
6,965 | 745 | Locally Adaptive Nearest Neighbor
Algorithms
Dietrich Wettschereck
Thomas G. Dietterich
Department of Computer Science
Oregon State University
Corvallis, OR 97331-3202
wettscdGcs.orst.edu
Abstract
Four versions of a k-nearest neighbor algorithm with locally adaptive k are introduced and compared to the basic k-neares... | 745 |@word repository:3 version:3 eliminating:1 norm:1 km:3 thereby:1 initial:1 contains:1 outperforms:3 must:4 subsequent:2 hypothesize:1 fewer:1 selected:1 consulting:1 ames:1 along:2 constructed:16 c2:4 become:1 overhead:1 paragraph:1 presumed:1 rapid:1 behavior:1 encouraging:1 actual:1 increasing:1 gift:1 cleveland... |
6,966 | 746 | A Local Algorithm to Learn Trajectories
with Stochastic Neural Networks
Javier R. Movellan?
Department of Cognitive Science
University of California San Diego
La Jolla, CA 92093-0515
Abstract
This paper presents a simple algorithm to learn trajectories with a
continuous time, continuous activation version of the Boltz... | 746 |@word effect:1 consisted:1 version:2 assigned:1 sinusoid:1 symmetric:1 simulation:1 tried:1 stochastic:10 pea:4 neal:2 rt:1 gradient:6 distance:2 require:2 simulated:1 generalization:1 dns:1 motion:2 activation:4 great:1 additive:1 consecutive:1 motor:2 adopt:1 negative:1 implementation:1 dampened:1 applicable:2 b... |
6,967 | 747 | An Optimization Method of Layered
Neural Networks based on the Modified
Information Criterion
Sumio Watanabe
Information and Communication R&D Center
Ricoh Co., Ltd.
3-2-3, Shin-Yokohama, Kohoku-ku, Yokohama, 222 Japan
sumio@ipe.rdc.ricoh.co.jp
Abstract
This paper proposes a practical optimization method for layered
n... | 747 |@word eliminating:2 jlf:1 rol:1 idl:3 initial:11 complexit:1 rpi:2 selected:1 steepest:1 sigmoidal:1 istical:1 theoretically:2 expected:1 actual:1 totally:1 estimating:1 generalizat:1 kaufman:1 minimizes:4 quantitative:1 xd:1 control:1 unit:8 positive:1 understood:2 local:3 modify:1 io:1 yd:1 studied:1 mateo:1 co:... |
6,968 | 748 | Recovering a Feed-Forward Net
From Its Output
Charles Fefferman * and Scott Markel
David Sarnoff Research Center
CN5300
Princeton, NJ 08543-5300
e-mail: cf9imath.princeton .edu
smarkel@sarnoff.com
ABSTRACT
We study feed-forward nets with arbitrarily many layers, using the standard sigmoid, tanh x. Aside from technical... | 748 |@word briefly:1 isil:1 open:2 pick:1 carry:1 moment:1 reduction:3 united:1 com:1 comparing:1 nt:1 analytic:12 aside:1 nervous:2 plane:1 xk:3 ith:1 node:18 qualitative:1 consists:1 theoretically:1 ote:1 little:1 begin:1 project:1 notation:1 albertini:1 kind:1 interpreted:1 transformation:1 nj:2 every:1 control:1 ap... |
6,969 | 749 | Optimal Brain Surgeon:
Extensions and performance comparisons
Babak Hassibi*
David G. Stork
Gregory Wolff
Takahiro Watanabe
Ricoh California Research Center
2882 Sand Hill Road Suite 115
Menlo Park, CA 94025-7022
and
* Department of Electrical Engineering
105B Durand Hall
Stanford University
Stanford, CA 94305-4055... | 749 |@word version:1 norm:4 seems:2 retraining:11 humidity:1 simulation:3 decomposition:4 covariance:2 thereby:1 contains:1 document:1 qth:2 comparing:2 com:1 subsequent:1 informative:1 fewer:2 monk:2 xk:3 isotropic:1 equi:1 simpler:1 five:1 mathematical:1 along:1 incorrect:1 wild:1 fitting:1 indeed:1 expected:1 roughl... |
6,970 | 75 | 310
PROBABILISTIC CHARACTERIZATION OF
NEURAL MODEL COMPUTATIONS
Richard M. Golden t
University of Pittsburgh, Pittsburgh, Pa. 15260
ABSTRACT
Information retrieval in a neural network is viewed as a procedure in
which the network computes a "most probable" or MAP estimate of the unknown information. This viewpoint allo... | 75 |@word mild:1 version:2 briefly:1 covariance:1 fonn:1 smolen:2 substitution:3 subjective:15 activation:7 dx:1 must:1 written:1 additive:2 designed:3 stationary:1 provides:1 characterization:1 mathematical:2 constructed:1 direct:1 become:1 retrieving:1 inter:1 pf:26 provided:2 estimating:1 mass:1 null:1 interpreted:2... |
6,971 | 750 | Mixtures of Controllers for
Jump Linear and Non-linear Plants
Timothy W. Cacciatore
Department of Neurosciences
University of California at San Diego
La Jolla, CA 92093
Steven J. Nowlan
Synaptics, Inc.
2698 Orchard Parkway
San Jose, CA 95134
Abstract
We describe an extension to the Mixture of Experts architecture fo... | 750 |@word bf:2 simulation:1 decomposition:3 jacob:6 tr:1 selecting:1 past:1 current:1 nowlan:8 must:1 i1l:1 asymptote:1 designed:3 update:1 stationary:6 sys:1 toronto:1 direct:1 become:1 incorrect:2 headed:1 behavior:14 frequently:1 automatically:2 actual:4 inappropriate:1 becomes:3 provided:3 underlying:1 linearity:2... |
6,972 | 751 | Inverse Dynamics
of Speech Motor Control
Makoto Hirayama Eric Vatikiotis-Datesol1 Mitsuo Kawato"
ATR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan
Abstract
Progress ha.s been made in comput.ational implementation of speech
production based on physiologica... | 751 |@word llsed:1 kura:1 compression:1 pulse:2 simulation:1 t_:1 contraction:1 rol:1 thereby:1 blade:1 reduction:1 initial:2 series:2 recovered:1 anterior:1 cooker:1 activation:3 synthesizer:3 toh:1 must:1 readily:1 realistic:2 motor:14 zacks:2 plot:2 nemal:1 update:1 yoh:1 filtered:1 honda:2 along:4 direct:4 symposiu... |
6,973 | 752 | Dual Mechanisms for Neural Binding
and Segmentation
Paul Sajda and Leif H. Finkel
Department of Bioengineering and
Institute of Neurological Science
University of Pennsylvania
220 South 33rd Street
Philadelphia, PA . 19104-6392
Abstract
We propose that the binding and segmentation of visual features
is mediated by tw... | 752 |@word illustrating:1 stronger:1 closure:8 simulation:11 accounting:1 extrastriate:2 configuration:1 efficacy:1 interestingly:1 comparing:2 od:3 si:1 activation:9 assigning:1 numerical:1 shape:2 plot:2 discrimination:3 cue:6 mental:1 location:1 x128:1 along:4 c2:1 brain:1 td:2 retinotopic:1 bounded:1 underlying:1 c... |
6,974 | 753 | Neural Network Methods for
Optimization Problems
Arun Jagota
Department of Mathematical Sciences
Memphis State University
Memphis, TN 38152
E-mail: jagota~nextl.msci.memst.edu
In a talk entitled "Trajectory Control of Convergent Networks with applications
to TSP", Natan Peterfreund (Computer Science, Technion) dealt ... | 753 |@word implemented:1 conquer:1 middle:1 functioning:1 hypercube:1 objective:1 move:1 symmetric:1 peterfreund:1 damage:1 lobe:1 human:1 responds:1 said:1 self:1 gradient:2 covering:1 whereby:1 simulated:5 dimacs:1 criterion:1 nondifferentiable:1 syntax:2 mail:1 daniel:1 tuned:1 biological:2 reason:1 tn:1 current:1 i... |
6,975 | 754 | Fast Pruning Using Principal
Components
Asriel U. Levin, Todd K. Leen and John E. Moody
Department of Computer Science and Engineering
Oregon Graduate Institute
P.O. Box 91000
Portland, OR 97291-1000
Abstract
We present a new algorithm for eliminating excess parameters and
improving network generalization after super... | 754 |@word briefly:1 manageable:1 eliminating:5 polynomial:4 norm:1 retraining:3 simulation:1 linearized:2 covariance:2 contraction:1 solid:2 reduction:1 series:2 efficacy:1 existing:1 activation:2 pcp:18 must:2 john:1 numerical:1 additive:1 cheap:3 remove:4 stationary:1 half:1 ith:3 short:1 math:1 node:9 successive:1 ... |
6,976 | 755 | WATTLE: A Trainable Gain Analogue
VLSI Neural Network
Richard Coggins and Marwan Jabri
Systems Engineering and Design Automation Laboratory
Department of Electrical Engineering J03,
University of Sydney, 2006.
Australia.
Email: richardc@sedal.su.oz.au
marwan@sedal.su.oz.au
Abstract
This paper describes a low power ana... | 755 |@word sydney:1 implemented:4 effect:6 verify:1 indicate:1 achievable:1 tester:1 read:1 capacitance:1 laboratory:1 modifying:1 simulation:1 australia:1 transient:6 ll:1 during:2 thereby:1 separate:2 rhythm:1 die:1 feeding:1 m:1 consumption:1 investigation:1 demonstrate:1 summation:1 coggins:4 richardc:1 current:12 ... |
6,977 | 756 | Lower Boundaries of Motoneuron
Desynchronization via Renshaw Interneurons
Mitchell Gil Maltenfort
It
Robert E. Druzinsky
Dept. of Physiology
Northwestern University
Chicago, IT.. 60611
Dept. of Biomedical Engineering
Northwestern University
Evanston, IT.. 60201
c.
w.
J. Heckman
Zev Rymer
Dept. of Physiology
and... | 756 |@word trial:1 seems:1 open:1 r:1 simulation:3 excited:1 solid:1 initial:1 series:3 contains:1 suppressing:1 past:2 current:13 surprising:1 activation:13 physiol:6 realistic:3 visible:1 chicago:5 interspike:2 discernible:1 motor:8 remove:1 plot:4 medial:2 clumping:1 v:2 nervous:1 renshaw:8 rc:23 along:2 direct:1 di... |
6,978 | 757 | Fool.s Gold: Extracting Finite State Machines
From Recurrent Network Dynamics
John F. Kolen
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
The Ohio State University
Columbus,OH 43210
kolen-j @cis.ohio-state.edu
Abstract
Several recurrent networks have been proposed as re... | 757 |@word version:1 laurence:1 initial:14 contains:1 current:4 discretization:2 activation:4 yet:2 must:1 john:3 periodically:1 visible:1 partition:2 shape:2 remove:1 intelligence:3 device:1 beginning:1 short:1 quantized:1 location:2 unbounded:1 mathematical:2 along:2 constructed:2 pathway:1 nondeterministic:5 behavio... |
6,979 | 758 | Neural Network Definitions of Highly
Predictable Protein Secondary Structure
Classes
Alan Lapedes
Complex Systems Group (TI3)
LANL, MS B213 Los Alamos N.M. 87545
and The Santa Fe Institute, Santa Fe, New Mexico
Evan Steeg
Department of Computer Science
University of Toronto, Toronto, Canada
Robert Farber
Complex System... | 758 |@word cu:1 version:1 simulation:3 disappointingly:1 initial:6 denoting:1 lapedes:12 past:3 yet:1 shape:1 discrimination:2 intelligence:2 discovering:1 ith:1 tertiary:1 toronto:3 zhang:6 five:1 mathematical:1 along:1 beta:11 become:1 viable:1 yuan:1 roughly:1 behavior:1 examine:1 multi:1 window:14 totally:1 project... |
6,980 | 759 | Tonal Music as a Componential Code:
Learning Temporal Relationships Between and
Within Pitch and Timing Components
Catherine Stevens
Department of Psychology
University of Queensland
QLD 4072 Australia
kates@psych.psy.uq.oz.au
Janet Wiles
Depts of Psychology & Computer Science
University of Queensland
QLD 4072 Austra... | 759 |@word polynomial:2 simulation:1 queensland:5 initial:1 feulner:2 reaction:1 chordal:3 activation:8 must:2 reminiscent:1 j1:8 designed:1 fund:1 pylyshyn:2 alone:4 half:5 device:1 guess:1 tone:10 beginning:1 provides:1 plaut:2 five:1 along:2 constructed:1 become:1 notably:1 expected:5 elman:4 frequently:1 abscissa:1... |
6,981 | 76 | 750
A DYNAMICAL APPROACH TO TEMPORAL PATTERN
PROCESSING
W. Scott Stornetta
Stanford University, Physics Department, Stanford, Ca., 94305
Tad Hogg and B. A. Huberman
Xerox Palo Alto Research Center, Palo Alto, Ca. 94304
ABSTRACT
Recognizing patterns with temporal context is important for
such tasks as speech recognitio... | 76 |@word retraining:2 suitably:1 pulse:3 tr:1 solid:1 initial:2 lapedes:1 past:2 current:4 contextual:1 activation:1 yet:1 must:3 john:1 distant:1 remove:1 discrimination:2 half:1 accordingly:1 record:1 sudden:1 node:34 successive:1 height:1 along:1 direct:1 replication:2 consists:2 manner:1 introduce:1 rapid:1 themse... |
6,982 | 760 | Processing of Visual and Auditory Space
and Its Modification by Experience
Josef P. Rauschecker
Laboratory of Neurophysiology
National Institute of Mental Health
Poolesville, MD 20837
Terrence J. Sejnowski
Computational Neurobiology Lab
The Salk: Institute
San Diego, CA 92138
Visual spatial information is projected f... | 760 |@word neurophysiology:1 blindness:1 flesh:1 move:1 question:1 laboratory:1 md:1 lobe:1 opinion:1 mammal:1 owl:5 inferior:1 profit:1 lateral:1 biological:1 rearing:1 extension:1 bring:2 noradrenaline:1 normal:2 visually:1 must:1 superior:2 realistic:1 sharper:1 plasticity:2 early:2 motor:1 jp:2 cerebral:2 cue:2 gat... |
6,983 | 761 | Hidden Markov Models for Human
Genes
Pierre Baldi *
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
Yves Chauvin t
Net-ID, Inc.
601 Minnesota
San Francisco, CA 94107
S0ren Brunak
Center for Biological Sequence Analysis
The Technical University of Denmark
DK-2800 Lyngby, Denmark
Jacob En... | 761 |@word seems:4 jacob:1 harder:1 electronics:1 initial:1 score:1 genetic:3 lapedes:2 current:2 yet:1 must:1 parsing:7 cruz:1 subsequent:1 plot:1 discrimination:1 alone:1 histone:1 beginning:1 short:5 detecting:2 ucsc:1 become:1 consists:2 combine:1 baldi:11 inside:1 concerted:1 indeed:1 roughly:3 themselves:2 codon:... |
6,984 | 762 | Cross-Validation Estimates IMSE
Mark Plutowski
t*
Shinichi Sakata t
Halbert White
t*
t Department of Computer Science and Engineering
t Department of Economics
* Institute for
Neural Computation
University of California, San Diego
Abstract
Integrated Mean Squared Error (IMSE) is a version of the usual
mean squa... | 762 |@word mild:1 version:5 stronger:2 dekker:1 adrian:3 concise:1 thereby:1 minus:1 liu:1 series:2 selecting:3 dx:2 written:1 must:1 john:1 cottrell:1 mackey:1 intelligence:1 selected:1 ith:4 math:2 location:1 unbiasedly:1 mathematical:1 bowman:3 direct:1 supply:1 symp:1 underfitting:1 introduce:1 homoscedasticity:1 e... |
6,985 | 763 | Segmental Neural Net Optimization for Continuous Speech
Recognition
Ymg Zhao
Richard Schwartz
John Makhoul
George Zavaliagkos
BBN System and Technologies
70 Fawcett Street
Cambridge MA 02138
Abstract
Previously, we had developed the concept of a Segmental Neural Net (SNN) for
phonetic modeling in continuous speec... | 763 |@word version:1 bigram:2 nd:5 gish:1 decomposition:3 tr:1 reduction:2 initial:4 series:1 score:6 selecting:1 current:2 comparing:1 nt:1 john:1 partition:1 designed:1 plot:1 alone:2 tenn:2 half:2 core:1 provides:1 rescoring:3 hyperplanes:4 sigmoidal:6 dn:1 consists:2 combine:1 theoretically:1 snn:20 project:1 kind:... |
6,986 | 764 | Convergence of Stochastic Iterative
Dynamic Programming Algorithms
Tommi Jaakkola'"
Michael I. Jordan
Satinder P. Singh
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Increasing attention has recently been paid to algorithms based on
dynamic programming (... | 764 |@word version:9 norm:7 contraction:11 paid:1 thereby:3 yvt:1 past:3 current:1 written:1 readily:2 fn:11 numerical:1 update:5 maxv:1 implying:1 tdp:1 characterization:1 mathematical:3 symposium:1 prove:1 behavioral:1 manner:1 peng:2 indeed:1 expected:3 brain:1 terminal:3 bellman:2 discounted:1 td:21 increasing:2 be... |
6,987 | 765 | Neural Network Exploration Using
Optimal Experiment Design
David A. Cohn
Dept. of Brain and Cognitive Sciences
Massachusetts Inst. of Technology
Cambridge, MA 02139
Abstract
Consider the problem of learning input/output mappings through
exploration, e.g. learning the kinematics or dynamics of a robotic
manipulator. I... | 765 |@word inversion:1 retraining:1 simulation:1 tried:1 covariance:4 concise:1 moment:1 initial:1 selecting:6 current:3 discretization:1 lang:2 yet:1 must:11 additive:1 informative:1 cheap:1 compution:1 asymptote:1 designed:1 plot:1 update:1 atlas:1 v:1 greedy:10 selected:1 provides:1 toronto:1 successive:1 simpler:1 ... |
6,988 | 766 | Odor Processing in the Bee: a Preliminary
Study of the Role of Central Input to the
Antennal Lobe.
Christiane Linster
David Marsan
ESPeI, Laboratoire d'Electronique
10, Rue Vauquelin, 75005 Paris
linster@neurones.espci.fr
Claudine Masson
Michel Kerszberg
Laboratoire de Neurobiologie Comparee
des Invertebrees
INRNCNR... | 766 |@word hyperpolarized:1 open:1 simulation:1 lobe:20 excited:3 reentrant:1 reduction:2 interestingly:2 odour:3 activation:7 mushroom:10 physiol:1 realistic:3 plasticity:1 discrimination:3 location:1 direct:2 differential:2 bouquet:1 pathway:3 olfactory:24 introduce:2 behavior:2 morphology:1 brain:2 freeman:1 schild:... |
6,989 | 767 | Supervised learning from incomplete
data via an EM approach
Zoubin Ghahramani and Michael I. Jordan
Department of Brain & Cognitive Sciences
Massachusett.s Institute of Technology
Cambridge, MA 02139
Abstract
Real-world learning tasks may involve high-dimensional data sets
with arbitrary patterns of missing data. In ... | 767 |@word repository:1 duda:2 proportion:2 covariance:4 jacob:6 xilzij:2 pick:1 ld:1 moment:1 series:1 zij:9 selecting:1 denoting:1 current:3 comparing:1 od:1 nowlan:3 must:4 written:1 readily:1 belmont:1 partition:1 analytic:1 enables:1 aside:1 alone:1 pursued:1 along:1 combine:2 fitting:1 expected:1 brain:1 fwm:1 li... |
6,990 | 768 | Dopaminergic Neuromodulation Brings a
Dynamical Plasticity to the Retina
Eric Boussard
Jean-Fran~ois
Vibert
B3E, INSERM U263
Faculte de medecine Saint-Antoine
27 rue Chaligny
75571 Paris cedex 12
Abstract
The fovea of a mammal retina was simulated with its detailed biological properties to study the local preproce... | 768 |@word illustrating:1 middle:1 disk:4 grey:1 simulation:5 brightness:6 mammal:2 exclusively:1 tuned:1 current:1 must:1 plasticity:5 enables:1 stationary:3 nervous:1 provides:2 constructed:1 direct:2 boycott:2 pathway:6 behavior:3 decreasing:1 vertebrate:2 becomes:1 retinotopic:1 panel:1 what:1 vanished:1 temporal:5... |
6,991 | 769 | Signature Verification using a "Siamese"
Time Delay Neural Network
Jane Bromley, Isabelle Guyon, Yann LeCun,
Eduard Sickinger and Roopak Shah
AT&T Bell Laboratories
Holmdel, NJ 07733
jbromley@big.att.com
Copyrighte, 1994, American Telephone and Telegraph Company used by permission.
Abstract
This paper describes an al... | 769 |@word trial:1 version:1 compression:1 proportion:1 duda:2 nd:1 open:1 simulation:1 eng:1 pressure:1 harder:1 carry:1 liu:3 att:1 practiced:1 past:1 com:1 comparing:2 lang:2 written:1 must:7 john:2 numerical:1 shape:2 wanted:1 remove:2 designed:1 discrimination:1 resampling:1 device:3 imitate:2 updatable:1 ith:1 pr... |
6,992 | 77 | 534
The Performance of Convex Set projection Based Neural Networks
Robert J. Marks II, Les E. Atlas, Seho Oh and James A. Ritcey
Interactive Systems Design Lab, FT-IO
University of Washington, Seattle, Wa 98195.
ABSTRACT
We donsider a class of neural networks whose performance can be
analyzed
and
geometrically
visua... | 77 |@word cylindrical:2 km:1 eng:1 reduction:1 envision:1 written:2 numerical:2 partition:6 atlas:2 plot:4 discrimination:1 stationary:1 intelligence:1 v:1 plane:1 transposition:1 provides:1 node:4 ron:1 sigmoidal:1 ik:1 prove:2 redefine:1 manner:3 indeed:1 roughly:1 globally:1 increasing:1 project:1 notation:5 circuit... |
6,993 | 770 | Packet Routing in Dynamically
Changing Networks:
A Reinforcement Learning Approach
Justin A. Boyan
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Michael L. Littman?
Cognitive Science Research Group
Bellcore
Morristown, NJ 07962
Abstract
This paper describes the Q-routing algorithm for pa... | 770 |@word trial:1 version:1 rising:1 seems:1 simulation:5 tried:1 thereby:1 initial:3 inefficiency:3 t7:1 past:1 outperforms:1 current:2 com:1 surprising:1 router:1 visible:1 realistic:1 periodically:1 plot:1 update:1 v:2 congestion:8 half:2 discovering:1 greedy:1 rudin:1 slowing:1 accordingly:1 node:19 contribute:1 i... |
6,994 | 771 | Resolving motion ambiguities
K. I. Diamantaras
Siemens Corporate Research
755 College Rd . East
Princeton, NJ 08540
D. Geiger*
Courant Institute, NYU
Mercer Street
New York, NY 10012
Abstract
We address the problem of optical flow reconstruction and in particular the problem of resolving ambiguities near edges. They... | 771 |@word stronger:1 covariance:2 pick:1 brightness:1 configuration:1 contains:1 must:1 partition:1 j1:2 girosi:1 shape:1 stationary:3 half:1 intelligence:1 plane:1 beginning:1 ladendorf:1 along:7 become:1 inside:5 introduce:3 expected:1 roughly:2 resolve:3 little:1 window:2 considering:1 moreover:1 mass:2 nj:1 guaran... |
6,995 | 772 | Optimal Stochastic Search and
Adaptive Momentum
Todd K. Leen and Genevieve B. Orr
Oregon Graduate Institute of Science and Technology
Department of Computer Science and Engineering
P.O.Box 91000, Portland, Oregon 97291-1000
Abstract
Stochastic optimization algorithms typically use learning rate
schedules that behave ... | 772 |@word norm:3 simulation:9 covariance:1 minus:1 solid:2 kappen:1 moment:2 series:2 john:3 christian:1 drop:5 plot:1 update:4 depict:1 provides:3 simpler:1 mathematical:2 dn:1 differential:1 symposium:1 behavior:11 themselves:1 elman:1 automatically:1 td:1 becomes:1 dnv:1 insure:1 circuit:1 lowest:2 ttl:1 developed:... |
6,996 | 773 | Convergence of Indirect Adaptive
Asynchronous Value Iteration Algorithms
Vijaykumar Gullapalli
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
vijay@cs.umass.edu
Andrew G. Barto
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
barto@cs.umass.edu
Abstract
Reinf... | 773 |@word trial:4 version:2 norm:2 simulation:1 initial:1 series:1 uma:2 selecting:1 efficacy:1 outperforms:1 existing:4 current:3 nt:2 numerical:1 update:6 selected:3 item:1 provides:1 direct:9 prove:2 combine:1 peng:2 expected:9 behavior:1 examine:1 planning:1 discounted:1 td:2 actual:1 pf:3 confused:1 estimating:1 ... |
6,997 | 774 | GDS: Gradient Descent Generation of
Symbolic Classification Rules
Reinhard Blasig
Kaiserslautern University, Germany
Present address: Siemens AG, ZFE ST SN 41
81730 Miinchen, Germany
Abstract
Imagine you have designed a neural network that successfully learns
a complex classification task. What are the relevant input... | 774 |@word private:3 repository:2 advantageous:1 nd:1 attainable:2 concise:6 bourgine:2 contains:1 series:5 pub:1 genetic:1 current:1 discretization:7 activation:10 schnitger:2 designed:3 succeeding:2 nonsaturated:3 pursued:1 accordingly:1 beginning:1 prespecified:1 provides:1 math:1 node:7 miinchen:1 sigmoidal:6 therm... |
6,998 | 775 | Coupled Dynamics of Fast Neurons and
Slow Interactions
A.C.C. Coolen
R.W. Penney
D. Sherrington
Dept. of Physics - Theoretical Physics
University of Oxford
1 Keble Road, Oxford OXI 3NP, U.K.
Abstract
A simple model of coupled dynamics of fast neurons and slow interactions, modelling self-organization in recurrent neu... | 775 |@word briefly:2 version:1 kondor:2 r:1 q1:5 tr:1 solid:1 substitution:1 efficacy:1 paramagnetic:3 nt:1 yet:1 reminiscent:2 partition:5 entrance:2 enables:1 n_o:1 hamiltonian:3 provides:1 math:7 complication:1 direct:1 qualitative:1 consists:1 manner:1 expected:1 themselves:1 mechanic:1 inspired:1 adiabatically:1 u... |
6,999 | 776 | An Analog VLSI Saccadic Eye Movement
System
Timothy K. Horiuchi
Brooks Bishofberger and Christof Koch
Computation and Neural Systems Program
California Institute of Technology
MS 139-74
Pasadena, CA 91125
Abstract
In an effort to understand saccadic eye movements and their relation to visual attention and other forms... | 776 |@word middle:1 version:1 integrative:1 pulse:8 simulation:1 brightness:1 mammal:1 solid:1 carry:1 initial:3 current:7 yet:1 must:2 vor:3 motor:17 sponsored:1 v:4 nervous:2 shut:1 short:2 sudden:1 provides:2 location:3 sigmoidal:1 mathematical:1 burst:20 constructed:1 fixation:4 behavioral:1 fabricate:1 notably:1 b... |
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