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3,200 | 39 | 412
CAPACITY FOR PATTERNS AND SEQUENCES IN KANERVA'S SDM
AS COMPARED TO OTHER ASSOCIATIVE MEMORY MODELS
James O. Keeler
Chemistry Department, Stanford University, Stanford, CA 94305
and RIACS, NASA-AMES 230-5 Moffett Field, CA 94035.
e-mail: jdk@hydra.riacs.edu
ABSTRACT
The information capacity of Kanerva's Sparse, ... | 39 |@word middle:2 proportionality:2 contraction:1 simplifying:1 paid:1 versatile:2 moment:1 initial:1 configuration:1 cyclic:1 past:4 ka:1 recovered:1 contextual:1 skipping:1 activation:1 si:1 dx:1 must:4 written:1 john:3 riacs:4 numerical:1 j1:1 hts:1 v:1 alone:2 half:1 selected:3 device:1 tenn:1 sys:1 dissertation:1... |
3,201 | 390 | Speech Recognition using Connectionist Approaches
Khalid Choukri
SPRINT Coordinator
CAP GEMINI INNOVATION
118 rue de Tocqueville, 75017 Paris. France
e-mail: choukri@capsogeti.fr
Abstract
This paper is a summary of SPRINT project aims and results. The project
focus on the use of neuro-computing techniques to tackle v... | 390 |@word middle:1 seems:1 tried:1 multiedit:2 initial:1 contains:2 score:9 current:2 lang:1 remove:1 alone:2 instantiate:1 item:1 funahashi:3 provides:1 mathematical:2 along:1 consists:1 combine:1 inter:1 roughly:1 p1:1 examine:2 multi:2 sud:1 automatically:2 encouraging:1 little:1 param:1 window:2 project:9 provided... |
3,202 | 3,900 | SpikeAnts, a spiking neuron network modelling the
emergence of organization in a complex system
Sylvain Chevallier
TAO, INRIA-Saclay
Univ. Paris-Sud
F-91405 Orsay, France
sylchev@lri.fr
H?el`ene Paugam-Moisy
LIRIS, CNRS
Univ. Lyon 2
F-69676 Bron, France
hpaugam@liris.cnrs.fr
Mich`ele Sebag
TAO, LRI ? CNRS
Univ. Pari... | 3900 |@word neurophysiology:2 middle:1 open:1 grey:1 simulation:6 pulse:1 accounting:3 thereby:2 moment:1 phy:1 sociaux:1 liu:1 series:1 liquid:1 interestingly:1 current:4 surprising:1 must:1 numerical:2 visible:2 periodically:2 plasticity:5 realistic:1 enables:2 v:1 cue:5 signalling:1 accordingly:2 plane:2 ith:2 core:... |
3,203 | 3,901 | Random Projections for k-means Clustering
Christos Boutsidis
Department of Computer Science
RPI
Anastasios Zouzias
Department of Computer Science
University of Toronto
Petros Drineas
Department of Computer Science
RPI
Abstract
This paper discusses the topic of dimensionality reduction for k-means clustering. We pro... | 3901 |@word version:2 knd:2 norm:6 stronger:1 nd:8 seems:2 sammon:1 decomposition:2 elisseeff:1 moment:1 reduction:14 contains:1 score:6 selecting:1 denoting:2 interestingly:1 existing:1 ka:12 chazelle:1 rpi:2 numerical:3 partition:3 subsequent:1 kdd:1 drop:1 plot:2 v:3 accordingly:1 core:1 toronto:1 five:3 dn:1 constr... |
3,204 | 3,902 | Online Learning for Latent Dirichlet Allocation
David M. Blei
Department of Computer Science
Princeton University
Princeton, NJ
blei@cs.princeton.edu
Matthew D. Hoffman
Department of Computer Science
Princeton University
Princeton, NJ
mdhoffma@cs.princeton.edu
Francis Bach
INRIA?Ecole Normale Sup?erieure
Paris, Fran... | 3902 |@word version:1 pw:3 proportion:1 nd:13 open:1 seek:1 tried:1 series:1 score:1 ecole:1 document:57 outperforms:1 current:1 wd:1 nt:13 comparing:2 must:2 written:1 periodically:1 subsequent:1 partition:1 kdd:2 update:8 aside:1 stationary:8 generative:1 selected:1 half:1 intelligence:2 leaf:1 mccallum:2 ith:1 blei:... |
3,205 | 3,903 | Constructing Skill Trees for Reinforcement Learning
Agents from Demonstration Trajectories
George Konidaris? Scott Kuindersma?? Andrew Barto? Roderic Grupen?
Autonomous Learning Laboratory? Laboratory for Perceptual Robotics?
Computer Science Department, University of Massachusetts Amherst
{gdk, scottk, barto, grupen}... | 3903 |@word trial:1 middle:1 polynomial:1 nd:1 tadepalli:1 open:3 termination:4 mehta:2 incurs:2 thereby:2 solid:1 accommodate:1 initial:3 liu:4 series:2 uma:2 selecting:1 lqr:1 ours:1 existing:2 current:2 si:1 assigning:1 must:3 john:1 numerical:1 motor:1 designed:1 resampling:1 intelligence:4 fewer:2 selected:2 isotr... |
3,206 | 3,904 | Guaranteed Rank Minimization via Singular Value
Projection
Prateek Jain
Microsoft Research Bangalore
Bangalore, India
prajain@microsoft.com
Raghu Meka
UT Austin Dept. of Computer Sciences
Austin, TX, USA
raghu@cs.utexas.edu
Inderjit Dhillon
UT Austin Dept. of Computer Sciences
Austin, TX, USA
inderjit@cs.utexas.edu
... | 3904 |@word compression:1 norm:17 stronger:2 nd:1 linearized:1 decomposition:5 incurs:2 contains:1 lightweight:1 series:1 ours:1 existing:5 recovered:1 com:1 toh:2 danny:1 realistic:3 kdd:1 hypothesize:2 plot:7 designed:1 update:5 selected:1 fewer:2 propack:3 iterates:10 math:1 org:1 mathematical:1 along:3 direct:1 bec... |
3,207 | 3,905 | Humans Learn Using Manifolds, Reluctantly
Bryan R. Gibson, Xiaojin Zhu, Timothy T. Rogers? , Charles W. Kalish? , Joseph Harrison?
Department of Computer Sciences, ? Psychology, and ? Educational Psychology
University of Wisconsin-Madison, Madison, WI 53706 USA
{bgibson, jerryzhu}@cs.wisc.edu
{ttrogers, cwkalish, jcha... | 3905 |@word trial:2 middle:1 stronger:1 proportion:1 seems:1 nd:1 seek:1 propagate:1 covariance:1 pick:1 mammal:2 initial:1 score:1 selecting:2 tuned:1 document:1 interestingly:1 comparing:5 john:1 stemming:1 visible:1 remove:1 designed:3 hypothesize:1 plot:3 v:7 half:4 intelligence:1 item:23 p7:1 beginning:1 ith:1 fa9... |
3,208 | 3,906 | A New Probabilistic Model for Rank Aggregation
Tao Qin
Microsoft Research Asia
taoqin@microsoft.com
Xiubo Geng
Chinese Academy of Sciences
xiubogeng@gmail.com
Tie-Yan Liu
Microsoft Research Asia
tyliu@microsoft.com
Abstract
This paper is concerned with rank aggregation, which aims to combine multiple
input rankings... | 3906 |@word polynomial:4 decomposition:2 versatile:4 liu:2 contains:1 score:10 selecting:1 series:2 daniel:1 manmatha:1 document:1 outperforms:2 rath:1 com:4 gmail:1 written:1 enables:1 remove:2 drop:1 ainen:1 generative:2 selected:2 item:1 samplingbased:1 provides:1 bijection:1 location:7 firstly:1 five:1 mathematical... |
3,209 | 3,907 | Efficient Optimization for Discriminative
Latent Class Models
Armand Joulin?
INRIA
23, avenue d?Italie,
75214 Paris, France.
Francis Bach?
INRIA
23, avenue d?Italie,
75214 Paris, France.
Jean Ponce?
Ecole Normale Sup?erieure
45, rue d?Ulm
75005 Paris, France.
armand.joulin@inria.fr
francis.bach@inria.fr
jean.ponce... | 3907 |@word armand:2 briefly:1 inversion:1 polynomial:2 norm:1 version:1 logit:1 km:4 simulation:1 decomposition:1 jacob:1 tr:9 sepulchre:1 reduction:5 contains:1 score:1 ecole:2 document:3 denoting:2 kurt:1 outperforms:6 existing:3 com:1 nt:1 nowlan:1 assigning:1 must:1 john:2 shape:1 drop:3 designed:1 update:2 v:6 al... |
3,210 | 3,908 | Permutation Complexity Bound on Out-Sample Error
Malik Magdon-Ismail
Computer Science Department
Rensselaer Ploytechnic Institute
110 8th Street, Troy, NY 12180, USA
magdon@cs.rpi.edu
Abstract
We define a data dependent permutation complexity for a hypothesis set H, which
is similar to a Rademacher complexity or maxi... | 3908 |@word repository:2 briefly:1 nd:1 open:1 covariance:2 q1:1 asks:1 celebrated:1 selecting:2 chervonenkis:4 spambase:1 comparing:1 surprising:1 rpi:1 happen:1 ainen:3 drop:1 leaf:1 selected:2 item:1 multiset:1 complication:1 location:2 mcdiarmid:13 mathematical:1 along:1 constructed:1 direct:1 c2:1 symposium:4 beta... |
3,211 | 3,909 | Segmentation as Maximum-Weight Independent Set
William Brendel and Sinisa Todorovic
School of Electrical Engineering and Computer Science
Oregon State University
Corvallis, OR 97331
brendelw@onid.orst.edu, sinisa@eecs.oregonstate.edu
Abstract
Given an ensemble of distinct, low-level segmentations of an image, our goa... | 3909 |@word trial:1 middle:1 polynomial:1 seems:2 norm:1 suitably:1 bf:2 seek:4 decomposition:1 brightness:1 pick:1 initial:2 series:5 uncovered:1 selecting:3 ours:19 rightmost:1 outperforms:6 existing:1 current:1 ka:1 si:12 written:1 must:1 partition:4 shape:2 grass:1 v:1 greedy:1 selected:7 ith:2 provides:2 node:19 b... |
3,212 | 391 | Designing Linear Threshold Based Neural
Network Pattern Classifiers
Terrence L. Fine
School of Electrical Engineering
Cornell University
Ithaca, NY 14853
Abstract
The three problems that concern us are identifying a natural domain of
pattern classification applications of feed forward neural networks, selecting an ap... | 391 |@word km:3 seek:1 simulation:2 covariance:1 thereby:1 carry:1 reduction:2 series:1 contains:1 selecting:2 chervonenkis:3 pub:1 current:2 yet:1 assigning:1 must:1 subsequent:1 partition:3 informative:1 alphanumeric:1 realistic:1 enables:1 hypothesize:1 discrimination:1 implying:1 fewer:2 device:1 selected:2 ith:1 a... |
3,213 | 3,910 | A unified model of short-range and long-range
motion perception
Shuang Wu
Department of Statistics
UCLA
Los Angeles , CA 90095
shuangw@stat.ucla.edu
Xuming He
Department of Statistics
UCLA
Los Angeles , CA 90095
hexm@stat.ucla.edu
Hongjing Lu
Department of Psychology
UCLA
Los Angeles , CA 90095
hongjing@ucla.edu
Al... | 3910 |@word trial:2 norm:5 proportion:1 open:1 simulation:5 solid:1 recursively:4 configuration:1 contains:1 score:1 rightmost:4 contextual:1 gpu:1 readily:1 shape:1 enables:4 designed:1 plot:1 update:1 discrimination:2 stationary:1 cue:2 v:1 intelligence:1 short:14 coarse:2 detecting:1 node:34 location:1 preference:1 ... |
3,214 | 3,911 | Link Discovery using Graph Feature Tracking
Emile Richard
ENS Cachan - CMLA & MilleMercis, France
r.emile.richard@gmail.com
Nicolas Baskiotis
ENS Cachan - CMLA
nicolas.baskiotis@lip6.com
Theodoros Evgeniou
Technology Management and Decision Sciences,
INSEAD
Bd de Constance, Fontainebleau 77300, France
theodoros.evgen... | 3911 |@word h:2 version:1 norm:5 proportion:1 km:2 hu:1 simulation:6 linearized:3 pieter:1 decomposition:3 pick:1 tr:3 initial:1 series:2 score:3 kurt:1 past:6 existing:1 outperforms:3 current:1 com:2 si:2 gmail:1 bd:1 written:1 numerical:2 informative:2 shape:1 update:1 intelligence:2 discovering:1 website:2 selected:... |
3,215 | 3,912 | Categories and Functional Units: An Infinite
Hierarchical Model for Brain Activations
Danial Lashkari
Ramesh Sridharan
Polina Golland
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
{danial, rameshvs, polina}@csail.mit.edu
Abstract
We present a model th... | 3912 |@word trial:4 cox:1 mri:3 fusiform:1 proportion:1 seems:2 kriegeskorte:1 seek:1 lobe:1 simplifying:1 hsieh:2 splitmerge:1 initial:2 configuration:1 series:1 hemodynamic:1 interestingly:1 existing:1 comparing:1 activation:22 additive:1 partition:1 informative:1 confirming:1 shape:1 enables:2 haxby:1 remove:1 atlas... |
3,216 | 3,913 | A Primal-Dual Message-Passing Algorithm for
Approximated Large Scale Structured Prediction
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Tamir Hazan
TTI Chicago
hazan@ttic.edu
Abstract
In this paper we propose an approximated structured prediction framework for
large scale graphical models and derive message-passing ... | 3913 |@word version:1 norm:10 citeseer:3 sgd:5 ours:1 outperforms:3 existing:2 written:1 parsing:1 numerical:1 chicago:2 partition:10 update:3 stationary:3 intelligence:1 prohibitive:2 mccallum:2 vanishing:1 smith:1 iterates:3 boosting:1 node:6 allerton:1 daphne:1 unbounded:1 along:1 consists:1 prove:1 fitting:1 combin... |
3,217 | 3,914 | Spatial and anatomical regularization of SVM
for brain image analysis
R?emi Cuingnet
CRICM (UPMC/Inserm/CNRS), Paris, France
Inserm - LIF (UMR S 678), Paris, France
remi.cuingnet@imed.jussieu.fr
Habib Benali
Inserm - LIF, Paris, France
habib.benali@imed.jussieu.fr
Marie Chupin
CRICM, Paris, France
marie.chupin@upmc.f... | 3914 |@word kondor:3 mri:10 inversion:1 lobe:5 commute:1 series:1 loc:4 score:1 rkhs:1 discretization:1 activation:1 written:2 mesh:1 distant:2 shape:1 enables:3 atlas:14 medial:1 discrimination:3 selected:1 parameterization:2 isotropic:1 ith:3 short:3 mental:1 provides:4 node:4 hyperplanes:1 org:2 bopt:3 mathematical:... |
3,218 | 3,915 | An Alternative to Low-Level-Synchrony-Based
Methods for Speech Detection
Javier R. Movellan
University of California, San Diego
Machine Perception Laboratory
Atkinson Hall (CALIT2), 6100
9500 Gilman Dr., Mail Code 0440
La Jolla, CA 92093-0440
movellan@mplab.ucsd.edu
Paul Ruvolo
University of California, San Diego
Mac... | 3915 |@word briefly:1 seek:1 tried:1 citeseer:2 dramatic:1 versatile:2 hager:1 initial:1 contains:2 efficacy:1 series:1 document:4 reynolds:2 past:4 outperforms:1 imaginary:3 current:3 ka:1 si:4 periodically:1 visible:2 informative:1 realistic:1 v:1 half:5 selected:1 ruvolo:1 ith:2 short:1 provides:4 detecting:6 boosti... |
3,219 | 3,916 | Graph-Valued Regression
Han Liu Xi Chen John Lafferty Larry Wasserman
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Undirected graphical models encode in a graph G the dependency structure of a
random vector Y . In many applications, it is of interest to model Y given another random vector X as input. We re... | 3916 |@word version:1 briefly:1 middle:1 stronger:1 norm:3 seems:1 open:1 d2:3 simulation:3 covariance:12 mention:1 tr:5 recursively:2 liu:2 contains:1 score:2 selecting:1 tuned:1 prefix:2 recovered:1 yet:1 finest:1 john:1 partition:53 plot:1 treating:1 greedy:10 leaf:7 selected:2 plane:1 node:12 location:8 along:3 con... |
3,220 | 3,917 | Spike timing-dependent plasticity as dynamic filter
Joscha T. Schmiedt?, Christian Albers and Klaus Pawelzik
Institute for Theoretical Physics
University of Bremen
Bremen, Germany
schmiedt@uni-bremen.de, {calbers, pawelzik}@neuro.uni-bremen.de
Abstract
When stimulated with complex action potential sequences synapses ... | 3917 |@word neurophysiology:1 illustrating:1 version:1 cingulate:1 hippocampus:17 stronger:1 underline:2 physik:1 pulse:1 simulation:2 postsynaptically:1 paulsen:1 thereby:3 solid:1 reduction:1 initial:1 series:1 efficacy:1 tuned:1 suppressing:2 past:1 current:1 activation:4 yet:1 numerical:1 plasticity:14 shape:2 chri... |
3,221 | 3,918 | Feature Construction for Inverse Reinforcement
Learning
Zoran Popovi?c
University of Washington
zoran@cs.washington.edu
Sergey Levine
Stanford University
svlevine@cs.stanford.edu
Vladlen Koltun
Stanford University
vladlen@cs.stanford.edu
Abstract
The goal of inverse reinforcement learning is to find a reward functi... | 3918 |@word trial:5 middle:1 concise:1 tr:8 contains:3 existing:1 current:9 si:12 must:6 readily:2 realistic:1 subsequent:2 partition:1 enables:1 update:1 stationary:2 intelligence:1 leaf:5 selected:2 amir:1 indicative:1 merger:1 ith:4 core:1 accepting:1 num:1 provides:1 coarse:1 node:11 successive:1 five:1 along:1 con... |
3,222 | 3,919 | Active Instance Sampling via Matrix Partition
Yuhong Guo
Department of Computer & Information Sciences
Temple University
Philadelphia, PA 19122
yuhong@temple.edu
Abstract
Recently, batch-mode active learning has attracted a lot of attention. In this paper, we propose a novel batch-mode active learning approach that s... | 3919 |@word determinant:9 retraining:2 tedious:1 calculus:1 km:1 seek:4 covariance:14 pick:1 tr:3 initial:3 series:2 contains:1 selecting:3 crx:3 document:11 past:1 outperforms:2 current:2 comparing:2 attracted:1 written:1 john:1 numerical:1 partition:17 informative:8 update:1 v:11 greedy:3 selected:8 intelligence:1 fl... |
3,223 | 392 | Time Trials on Second-Order and
Variable-Learning-Rate Algorithms
Richard Rohwer
Centre for Speech Technology Research
Edinburgh University
80, South Bridge
Edinburgh EH 1 1HN, SCOTLAND
Abstract
The performance of seven minimization algorithms are compared on five
neural network problems. These include a variable-ste... | 392 |@word trial:3 proportion:1 pulse:4 barney:6 initial:2 contains:1 activation:1 si:1 numerical:5 analytic:4 scotland:1 ith:2 short:1 node:13 five:1 along:1 become:1 loll:1 incorrect:1 introduce:1 multi:1 little:1 becomes:2 bounded:1 watrous:3 suite:1 guarantee:2 act:1 esprit:1 control:1 positive:1 iqi:1 local:1 limi... |
3,224 | 3,920 | Reverse Multi-Label Learning
James Petterson
NICTA, Australian National University
Canberra, ACT, Australia
james.petterson@nicta.com.au
Tiberio Caetano
NICTA, Australian National University
Canberra, ACT, Australia
tiberio.caetano@nicta.com.au
Abstract
Multi-label classification is the task of predicting potentially... | 3920 |@word version:3 polynomial:1 pcc:3 justice:1 open:1 elisseeff:1 minus:1 reduction:1 initial:2 score:17 selecting:1 document:4 ours:3 existing:3 current:5 com:2 must:1 john:2 additive:2 hofmann:1 treating:1 plot:2 intelligence:1 selected:2 parameterization:1 boosting:1 penalises:1 herbrich:1 org:1 zhang:2 five:3 d... |
3,225 | 3,921 | More data means less inference: A pseudo-max
approach to structured learning
David Sontag
Microsoft Research
Ofer Meshi
Hebrew University
Tommi Jaakkola
CSAIL, MIT
Amir Globerson
Hebrew University
Abstract
The problem of learning to predict structured labels is of key importance in many
applications. However, for ... | 3921 |@word version:1 polynomial:3 stronger:1 open:2 tried:1 reduction:1 initial:1 configuration:5 contains:1 document:1 current:1 comparing:1 yet:1 dx:4 must:4 parsing:2 jkl:1 written:1 happen:1 partition:2 remove:1 drop:1 implying:1 generative:1 prohibitive:1 amir:1 parameterization:1 plane:10 yi1:1 smith:1 certifica... |
3,226 | 3,922 | On a Connection between Importance Sampling and
the Likelihood Ratio Policy Gradient
Jie Tang and Pieter Abbeel
Department of Electrical Engineering and Computer Science
University of California, Berkeley
Berkeley, CA 94709
{jietang, pabbeel}@eecs.berkeley.edu
Abstract
Likelihood ratio policy gradient methods have be... | 3922 |@word trial:26 kong:1 d2:2 pieter:1 simulation:3 linearized:1 r:1 incurs:1 recursively:2 reduction:4 initial:9 liu:1 tuned:1 lqr:6 rightmost:1 past:16 outperforms:3 existing:1 current:5 dx:1 must:1 readily:1 christian:1 motor:3 plot:5 designed:1 update:3 stationary:1 intelligence:2 fewer:2 selected:1 parameteriza... |
3,227 | 3,923 | Self-Paced Learning for Latent Variable Models
M. Pawan Kumar
Benjamin Packer
Daphne Koller
Computer Science Department
Stanford University
{pawan,bpacker,koller}@cs.stanford.edu
Abstract
Latent variable models are a powerful tool for addressing several tasks in machine
learning. However, the algorithms for learning ... | 3923 |@word illustrating:1 version:1 briefly:1 dalal:1 triggs:1 pick:1 mammal:3 thereby:1 initial:4 efficacy:1 selecting:1 score:5 sherali:1 document:5 outperforms:5 quadrilateral:1 current:2 z2:1 surprising:1 readily:2 john:1 visible:1 subsequent:1 numerical:1 shape:1 hofmann:1 treating:1 update:10 v:2 half:1 selected... |
3,228 | 3,924 | Fast Large-scale Mixture Modeling with
Component-specific Data Partitions
Bo Thiesson?
Microsoft Research
Chong Wang??
Princeton University
Abstract
Remarkably easy implementation and guaranteed convergence has made the EM
algorithm one of the most used algorithms for mixture modeling. On the downside,
the E-step is... | 3924 |@word msr:1 nd:2 km:5 decomposition:1 accounting:1 mention:1 tr:1 recursively:1 reduction:2 initial:9 series:1 ours:1 bradley:1 ka:2 current:2 com:1 written:1 must:1 refines:4 periodically:1 partition:66 plot:1 update:2 v:1 implying:2 intelligence:3 leaf:17 selected:1 fewer:1 accordingly:4 record:1 compo:1 coarse... |
3,229 | 3,925 | Static Analysis of Binary Executables Using
Structural SVMs
Nikos Karampatziakis?
Department of Computer Science
Cornell University
Ithaca, NY 14853
nk@cs.cornell.edu
Abstract
We cast the problem of identifying basic blocks of code in a binary executable as
learning a mapping from a byte sequence to a segmentation of ... | 3925 |@word version:4 middle:1 bigram:1 polynomial:1 seems:2 instruction:76 d2:1 tried:2 profit:2 harder:1 carry:1 reduction:2 contains:2 score:4 selecting:1 past:1 current:1 com:1 yet:2 tackling:1 written:1 parsing:2 john:1 chicago:1 happen:1 benign:1 hofmann:1 treating:3 siepel:1 greedy:3 selected:1 website:2 intelli... |
3,230 | 3,926 | Convex Multiple-Instance Learning by
Estimating Likelihood Ratio
Fuxin Li and Cristian Sminchisescu
Institute for Numerical Simulation, University of Bonn
{fuxin.li,cristian.sminchisescu}@ins.uni-bonn.de
Abstract
We propose an approach to multiple-instance learning that reformulates the problem as a convex optimizati... | 3926 |@word trial:1 seems:1 norm:1 flach:1 simulation:2 biconjugate:1 reduction:1 electronics:1 score:1 document:1 rkhs:3 current:1 babenko:1 yet:2 intriguing:1 numerical:2 hofmann:2 christian:1 drop:1 plot:1 treating:1 discrimination:1 stationary:1 implying:1 prohibitive:1 fewer:1 item:1 website:2 accordingly:1 plane:... |
3,231 | 3,927 | Distributionally Robust Markov Decision Processes
Huan Xu
ECE, University of Texas at Austin
huan.xu@mail.utexas.edu
Shie Mannor
Department of Electrical Engineering, Technion, Israel
shie@ee.technion.ac.il
Abstract
We consider Markov decision processes where the values of the parameters are
uncertain. This uncertai... | 3927 |@word mild:2 middle:1 briefly:2 polynomial:6 norm:1 termination:1 r:20 simulation:2 contraction:1 covariance:1 tr:1 moment:2 initial:1 celebrated:1 contains:2 series:1 outperforms:1 csn:1 current:2 si:2 yet:3 written:1 john:1 numerical:2 partition:1 hofmann:1 designed:1 v:1 stationary:20 generative:3 plane:2 prov... |
3,232 | 3,928 | t-Logistic Regression
Nan Ding2 , S.V. N. Vishwanathan1,2
Departments of 1 Statistics and 2 Computer Science
Purdue University
ding10@purdue.edu, vishy@stat.purdue.edu
Abstract
We extend logistic regression by using t-exponential families which were introduced recently in statistical physics. This gives rise to a reg... | 3928 |@word deformed:2 version:3 middle:1 seems:1 nd:1 tedious:1 open:1 vanhatalo:1 arti:1 eld:2 naudts:4 outlook:2 moment:1 initial:4 series:1 rightmost:1 existing:2 recovered:1 comparing:1 mushroom:5 dx:1 written:3 reminiscent:1 numerical:3 partition:3 kyb:1 plot:4 designed:2 v:1 cult:2 isotropic:2 core:1 gure:1 boos... |
3,233 | 3,929 | Deep Coding Network
Yuanqing Lin? Tong Zhang? Shenghuo Zhu? Kai Yu?
?
NEC Laboratories America, Cupertino, CA 95129
?
Rutgers University, Piscataway, NJ 08854
Abstract
This paper proposes a principled extension of the traditional single-layer flat
sparse coding scheme, where a two-layer coding scheme is derived based... | 3929 |@word mild:1 norm:6 seems:1 everingham:2 open:1 pick:1 accommodate:1 contains:2 interestingly:1 bradley:1 current:1 com:1 optim:1 attracted:1 partition:1 informative:2 remove:1 provides:2 codebook:15 location:1 org:1 simpler:1 zhang:3 consists:4 fitting:3 introduce:1 manner:2 theoretically:1 multi:6 salakhutdinov... |
3,234 | 393 | Reinforcenlent Learning in Markovian and
Non-Markovian Environments
Jiirgen Schmidhuber
Institut fiir Informatik
Technische Universitat Miinchen
Arcistr. 21, 8000 Miinchen 2, Germany
schmidhu@tumult.informatik.tu-muenchen.de
Abstract
This work addresses three problems with reinforcement learning and adaptive neuro-co... | 393 |@word trial:3 version:16 termination:1 jacob:2 tr:2 initial:1 past:1 current:8 activation:12 written:1 must:1 explorative:1 numerical:1 visible:2 update:1 stationary:1 deadlock:2 ith:3 indefinitely:1 compo:1 draft:1 miinchen:4 attack:1 five:1 mathematical:1 become:2 differential:2 incorrect:1 consists:1 ewe:1 mann... |
3,235 | 3,930 | Sparse Coding for Learning Interpretable
Spatio-Temporal Primitives
Taehwan Kim
TTI Chicago
taehwan@ttic.edu
Gregory Shakhnarovich
TTI Chicago
gregory@ttic.edu
Raquel Urtasun
TTI Chicago
rurtasun@ttic.edu
Abstract
Sparse coding has recently become a popular approach in computer vision to learn
dictionaries of natur... | 3930 |@word neurophysiology:1 middle:1 norm:22 confirms:1 simulation:1 q1:2 inpainting:1 ivaldi:2 series:3 ours:27 outperforms:7 existing:1 recovered:8 activation:30 belmont:1 chicago:3 partition:1 motor:4 plot:1 interpretable:12 depict:1 v:1 alone:2 greedy:2 discovering:1 selected:2 hallucinate:1 short:1 preference:1 ... |
3,236 | 3,931 | Moreau-Yosida Regularization for Grouped
Tree Structure Learning
Jun Liu
Computer Science and Engineering
Arizona State University
J.Liu@asu.edu
Jieping Ye
Computer Science and Engineering
Arizona State University
Jieping.Ye@asu.edu
Abstract
We consider the tree structured group Lasso where the structure over the fe... | 3931 |@word inversion:1 norm:4 jacob:2 boundedness:1 initial:1 liu:5 contains:5 series:4 outperforms:1 existing:1 written:1 subsequent:1 designed:2 update:2 n0:1 intelligence:4 asu:3 leaf:7 guess:1 kyk:1 selected:1 core:1 node:42 traverse:1 zhang:1 director:1 yuan:2 prove:2 introductory:1 introduce:1 indeed:2 nor:1 mul... |
3,237 | 3,932 | Transduction with Matrix Completion:
Three Birds with One Stone
Andrew B. Goldberg1 , Xiaojin Zhu1 , Benjamin Recht1 , Jun-Ming Xu1 , Robert Nowak2
Department of {1 Computer Sciences, 2 Electrical and Computer Engineering}
University of Wisconsin-Madison, Madison, WI 53706
{goldberg, jerryzhu, brecht, xujm}@cs.wisc.edu... | 3932 |@word trial:6 polynomial:1 seems:2 norm:11 nd:3 decomposition:1 elisseeff:2 kz1:2 reduction:1 initial:3 contains:1 exclusively:1 zij:10 series:1 daniel:1 tuned:4 interestingly:1 outperforms:1 current:1 recovered:2 z2:4 yet:1 zhu1:1 john:1 kdd:1 n0:1 alone:1 generative:3 item:20 beginning:1 lr:1 org:1 zhang:1 five... |
3,238 | 3,933 | Structured sparsity-inducing norms
through submodular functions
Francis Bach
INRIA - Willow project-team
Laboratoire d?Informatique de l?Ecole Normale Sup?erieure
Paris, France
francis.bach@ens.fr
Abstract
Sparse methods for supervised learning aim at finding good linear predictors from
as few variables as possible, ... | 3933 |@word illustrating:1 middle:3 version:1 polynomial:1 norm:91 armand:1 simulation:3 contraction:1 decomposition:3 covariance:3 jacob:1 tr:7 selecting:3 ecole:1 existing:1 current:1 readily:1 happen:1 partition:2 j1:4 shape:1 designed:3 interpretable:1 plot:3 v:6 greedy:12 pursued:1 xk:2 recherche:1 simpler:2 zhang... |
3,239 | 3,934 | Shadow Dirichlet for Restricted Probability Modeling
Bela A. Frigyik, Maya R. Gupta, and Yihua Chen
Department of Electrical Engineering
University of Washington
Seattle, WA 98195
frigyik@gmail.com, gupta@ee.washington.edu, yihuachn@gmail.com
Abstract
Although the Dirichlet distribution is widely used, the independen... | 3934 |@word m1j:1 inversion:1 achievable:1 proportion:6 open:1 simulation:3 covariance:4 idl:1 frigyik:3 moment:3 series:1 past:2 com:3 si:8 gmail:2 must:3 numerical:3 shape:1 enables:1 designed:1 treating:2 generative:1 rudin:1 ith:7 short:1 five:2 mathematical:1 direct:1 differential:2 introduce:1 expected:2 p1:1 fre... |
3,240 | 3,935 | Large Margin Multi-Task Metric Learning
Kilian Q. Weinberger
Department of Computer Science and Engineering
Washington University in St. Louis
St. Louis, MO 63130
kilian@wustl.edu
Shibin Parameswaran
Department of Electrical and Computer Engineering
University of California, San Diego
La Jolla, CA 92093
sparames@ucsd... | 3935 |@word multitask:4 deformed:1 repository:1 briefly:1 version:4 kulis:1 norm:1 mtlmnn:1 d2:2 integrative:1 pick:1 thereby:1 initial:1 contains:2 exclusively:1 outperforms:2 goldberger:1 kdd:3 remove:1 v:1 aside:1 selected:1 parameterization:1 xk:4 infrastructure:1 provides:2 hyperplanes:2 five:1 along:2 become:2 sp... |
3,241 | 3,936 | Relaxed Clipping: A Global Training Method
for Robust Regression and Classification
Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans
University of Alberta, Dept. Computing Science, Edmonton AB T6G 2E8, Canada
{yaoliang,myang2,linli,whitem,dale}@cs.ualberta.ca
Abstract
Robust regression and classificatio... | 3936 |@word logit:4 nd:2 accounting:1 elisseeff:1 ronchetti:2 tr:10 solid:4 boundedness:3 liu:1 interestingly:1 recovered:3 comparing:1 yet:1 written:2 john:1 realize:1 subsequent:1 seeding:1 plot:1 resampling:1 intelligence:1 warmuth:1 stahel:1 prespecified:1 provides:3 boosting:3 characterization:1 completeness:2 man... |
3,242 | 3,937 | A Rational Decision-Making Framework for Inhibitory Control
Rajesh P. N. Rao
Department of Computer Science
University of Washington
rao@cs.washington.edu
Pradeep Shenoy
Department of Cognitive Science
University of California, San Diego
pshenoy@ucsd.edu
Angela J. Yu
Department of Cognitive Science
University of Cal... | 3937 |@word neurophysiology:2 trial:96 version:1 advantageous:1 termination:1 simulation:7 p0:2 pressure:1 harder:1 recursively:1 moment:2 initial:3 subjective:1 reaction:10 past:1 current:6 yet:1 bd:1 must:1 subsequent:1 analytic:1 drop:3 discrimination:5 generative:1 fewer:5 rts:2 slowing:1 provides:1 detecting:1 awr... |
3,243 | 3,938 | Improvements to the Sequence Memoizer
Yee Whye Teh
Gatsby Computational Neuroscience Unit
University College London
London, WC1N 3AR, UK
ywteh@gatsby.ucl.ac.uk
Jan Gasthaus
Gatsby Computational Neuroscience Unit
University College London
London, WC1N 3AR, UK
j.gasthaus@gatsby.ucl.ac.uk
Abstract
The sequence memoizer... | 3938 |@word cu:34 compression:5 nd:1 d2:37 dramatic:1 recursively:2 contains:1 fragment:4 prefix:1 current:6 subsequent:1 numerical:2 drop:1 treating:2 update:1 instantiate:3 accordingly:1 beginning:1 short:1 pointer:1 memoizer:11 blei:1 iterates:1 math:1 node:2 sits:5 coagulation:11 firstly:1 org:1 along:2 direct:1 co... |
3,244 | 3,939 | Attractor Dynamics with Synaptic Depression
C. C. Alan Fung, K. Y. Michael Wong
Hong Kong University of Science and Technology, Hong Kong, China
alanfung@ust.hk, phkywong@ust.hk
He Wang
Tsinghua University, Beijing, China
wanghe07@mails.tsinghua.edu.cn
Si Wu
Institute of Neuroscience,
Chinese Academy of Sciences, Sha... | 3939 |@word kong:3 economically:1 version:1 stronger:2 seems:1 p0:20 solid:2 harder:1 colby:1 carry:1 initial:10 efficacy:2 reaction:2 hkust:1 si:1 yet:2 dx:5 ust:2 must:1 physiol:1 numerical:8 plasticity:1 shape:1 enables:2 displace:1 plot:2 update:1 overshooting:3 stationary:3 shut:3 realizing:2 short:8 core:1 provid... |
3,245 | 394 | Chaitin-Kolmogorov Complexity
and Generalization in Neural Networks
Barak A. Pearlmutter
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Ronald Rosenfeld
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We present a unified framework for a number of diffe... | 394 |@word illustrating:1 advantageous:1 replicate:5 disk:1 instruction:2 grey:1 simulation:1 ld:3 initial:2 must:2 tot:1 ronald:1 realistic:1 confirming:1 discrimination:1 attack:1 unbounded:1 constructed:2 replication:3 consists:1 overhead:1 manner:1 theoretically:1 expected:4 roughly:1 buying:1 considering:1 bounded... |
3,246 | 3,940 | Gaussian sampling by local perturbations
George Papandreou
Department of Statistics
University of California, Los Angeles
gpapan@stat.ucla.edu
Alan L. Yuille
Depts. of Statistics, Computer Science & Psychology
University of California, Los Angeles
yuille@stat.ucla.edu
Abstract
We present a technique for exact simula... | 3940 |@word version:2 additively:1 simulation:2 covariance:9 contrastive:3 q1:3 inpainting:6 versatile:1 shot:4 recursively:1 carry:1 reduction:2 tr:1 configuration:1 contains:2 score:1 selecting:2 mmse:3 existing:1 current:1 comparing:2 yet:1 assigning:1 must:1 gpu:2 readily:2 john:1 visible:13 numerical:3 periodicall... |
3,247 | 3,941 | Avoiding False Positive in Multi-Instance Learning
Yanjun Han, Qing Tao, Jue Wang
Institute of Automation, Chinese Academy of Sciences
Beijing, 100190, China
yanjun.han, qing.tao, jue.wang@ia.ac.cn
Abstract
In multi-instance learning, there are two kinds of prediction failure, i.e., false
negative and false positive.... | 3941 |@word version:1 duda:1 seems:2 flach:1 norm:1 seek:1 tried:1 citeseer:4 reduction:1 liu:1 contains:4 exclusively:1 document:2 rkhs:4 existing:1 current:3 com:1 si:2 assigning:1 chu:1 readily:1 john:1 subsequent:1 hofmann:2 treating:1 discrimination:2 grass:1 half:1 discovering:1 selected:2 intelligence:2 plane:4 ... |
3,248 | 3,942 | Computing Marginal Distributions over Continuous
Markov Networks for Statistical Relational Learning
Matthias Br?ocheler, Lise Getoor
University of Maryland, College Park
College Park, MD 20742
{matthias, getoor}@cs.umd.edu
Abstract
Continuous Markov random fields are a general formalism to model joint probability di... | 3942 |@word mild:1 nkb:1 polynomial:8 norm:1 stronger:1 d2:1 decomposition:1 p0:3 pick:1 thereby:1 boundedness:1 reduction:2 initial:7 contains:4 score:1 document:20 outperforms:2 existing:1 ka:3 current:3 surprising:1 dx:1 written:1 must:1 stemming:1 chicago:1 partition:1 hypothesize:1 plot:1 update:1 n0:6 v:1 intelli... |
3,249 | 3,943 | Optimal Bayesian Recommendation Sets and
Myopically Optimal Choice Query Sets
Paolo Viappiani?
Department of Computer Science
University of Toronto
paolo.viappiani@gmail.com
Craig Boutilier
Department of Computer Science
University of Toronto
cebly@cs.toronto.edu
Abstract
Bayesian approaches to utility elicitation t... | 3943 |@word version:1 logit:2 heuristically:1 simulation:1 lorraine:1 reduction:1 initial:2 configuration:4 cristina:1 selecting:3 daniel:1 offering:2 interestingly:1 bradley:1 com:1 gmail:1 must:4 john:1 refines:2 additive:4 partition:1 informative:2 treating:1 drop:2 update:4 greedy:29 prohibitive:1 discovering:1 ite... |
3,250 | 3,944 | Inter-time segment information sharing for
non-homogeneous dynamic Bayesian networks
Dirk Husmeier & Frank Dondelinger
Biomathematics & Statistics Scotland (BioSS)
JCMB, The King?s Buildings, Edinburgh EH93JZ, United Kingdom
dirk@bioss.ac.uk, frank@bioss.ac.uk
Sophie L`ebre
Universit?e de Strasbourg, LSIIT - UMR 7005,... | 3944 |@word briefly:1 proportion:2 stronger:2 grey:3 iki:3 simulation:4 tried:1 covariance:1 incurs:1 yih:6 biomathematics:1 reduction:1 configuration:1 series:22 contains:1 united:1 score:10 interestingly:1 existing:2 recovered:1 discretization:2 si:2 subsequent:1 partition:3 j1:1 realistic:1 shape:1 eleven:1 enables:... |
3,251 | 3,945 | Evaluation of Rarity of Fingerprints in Forensics
Chang Su and Sargur Srihari
Department of Computer Science and Engineering
University at Buffalo
Amherst, NY 14260
{changsu,srihari}@buffalo.edu
Abstract
A method for computing the rarity of latent fingerprints represented by minutiae
is given. It allows determining t... | 3945 |@word chakraborty:1 justice:1 covariance:6 configuration:2 contains:10 liu:1 united:2 offering:1 existing:1 comparing:1 si:1 subsequent:1 generative:14 selected:2 intelligence:1 item:1 inspection:1 ith:2 core:46 lr:3 provides:2 node:2 location:40 dn:1 dsn:5 consists:1 manner:1 expected:2 multi:2 chi:4 minutia:118... |
3,252 | 3,946 | Synergies in learning words and their referents
Katherine Demuth
Department of Linguistics
Macquarie University
Sydney, NSW 2109
Katherine.Demuth@mq.edu.au
Mark Johnson
Department of Computing
Macquarie University
Sydney, NSW 2109
Mark.Johnson@mq.edu.au
Michael Frank
Department of Psychology
Stanford University
Palo ... | 3946 |@word briefly:2 version:1 bigram:3 nd:4 reused:1 bn:2 nsw:2 pick:1 thereby:1 reduction:2 contains:1 score:9 document:1 interestingly:1 prefix:1 comparing:1 anne:1 analysed:2 activation:2 must:1 evans:1 cracking:1 infant:4 leaf:5 beginning:1 smith:1 record:1 blei:3 provides:1 node:9 gx:9 uppsala:1 simpler:1 mcfran... |
3,253 | 3,947 | Variational Bounds for Mixed-Data Factor Analysis
Mohammad Emtiyaz Khan
University of British Columbia
Vancouver, BC, Canada V6T 1Z4
emtiyaz@cs.ubc.ca
Guillaume Bouchard
Xerox Research Center Europe
38240 Meylan, France
guillaume.bouchard@xerox.com
Benjamin M. Marlin
University of British Columbia
Vancouver, BC, Can... | 3947 |@word repository:1 briefly:1 version:2 loading:7 covariance:8 ld:2 reduction:1 series:4 bc:3 longitudinal:1 existing:1 freitas:1 current:1 com:1 wd:3 ka:1 must:4 written:1 numerical:1 subsequent:1 enables:1 analytic:1 hypothesize:1 treating:1 update:4 v:4 generative:2 fewer:1 sutter:1 hamiltonian:4 blei:3 complet... |
3,254 | 3,948 | Functional form of motion priors in human motion
perception
Hongjing Lu 1,2
hongjing@ucla.edu
Tungyou Lin 3
tungyoul@math.ucla.edu
Alan L. F. Lee 1
alanlee@ucla.edu
Luminita Vese 3
lvese@math.ucla.edu
Alan Yuille 1,2,4
yuille@stat.ucla.edu
Department of Psychology1, Statistics2 , Mathematics3 and Computer Science4... | 3948 |@word trial:12 judgement:1 norm:22 proportion:2 seek:1 solid:2 harder:1 liu:1 series:1 comparing:2 discretization:2 surprising:1 must:2 readily:1 tilted:1 visible:1 underly:1 happen:1 blur:1 shape:1 enables:1 analytic:1 predetermined:1 designed:1 plot:11 update:1 discrimination:2 stationary:1 selected:1 nq:8 acco... |
3,255 | 3,949 | Inference with Multivariate Heavy-Tails
in Linear Models
Danny Bickson and Carlos Guestrin
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
{bickson,guestrin}@cs.cmu.edu
Abstract
Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possibl... | 3949 |@word kolaczyk:1 decomposition:1 moment:1 liu:1 united:1 nonparanormal:1 multiuser:2 existing:1 current:3 danny:1 must:1 dx:1 malized:1 w911nf0810242:1 planet:1 additive:1 numerical:1 john:1 realistic:1 partition:1 remove:1 plot:2 bickson:8 update:3 v:1 intelligence:2 accordingly:1 core:2 provides:1 math:1 node:1... |
3,256 | 395 | Back Propagation is Sensitive to Initial Conditions
John F. Kolen
Jordan B. Pollack
Laboratory for Artificial Intelligence Research
The Ohio State University
Columbus. OH 43210. USA
kolen-j@cis.ohio-state.edu
pollack@cis.ohio-state.edu
Abstract
This paper explores the effect of initial weight selection on feed-forwa... | 395 |@word trial:1 grey:1 minus:1 initial:31 configuration:3 past:1 must:2 john:1 partition:1 enables:1 plot:2 v:1 intelligence:1 tenn:1 plane:1 vanishing:1 num:1 node:1 location:1 successive:1 simpler:1 along:3 consists:1 inside:1 behavior:7 examine:1 metaphor:2 preclude:1 increasing:1 project:1 discover:1 linearity:1... |
3,257 | 3,950 | A Log-Domain Implementation of the Diffusion
Network in Very Large Scale Integration
Yi-Da Wu, Shi-Jie Lin, and Hsin Chen
Department of Electrical Engineering
National Tsing Hua University
Hsinchu, Taiwan 30013
{ydwu;hchen}@ee.nthu.edu.tw
Abstract
The Diffusion Network(DN) is a stochastic recurrent network which has ... | 3950 |@word tsing:1 trial:5 exploitation:1 cnn:1 inversion:1 compression:1 xof:9 nd:1 grey:1 simulation:17 solid:1 electronics:2 liu:1 contains:1 initial:1 mag:1 past:1 reaction:1 current:35 comparing:1 activation:1 regenerating:4 numerical:4 visible:11 ota:2 sdes:3 designed:3 v:1 device:2 xk:1 core:1 chua:1 num:1 firs... |
3,258 | 3,951 | Inference and communication in the game of
Password
Yang Xu? and Charles Kemp?
Machine Learning Department?
School of Computer Science?
Department of Psychology?
Carnegie Mellon University
{yx1@cs.cmu.edu, ckemp@cmu.edu}
Abstract
Communication between a speaker and hearer will be most efficient when both
parties make ... | 3951 |@word trial:1 norm:1 proportion:2 seems:2 bf:11 confirms:1 prominence:1 pressure:10 pick:1 reduction:2 initial:2 fragment:1 bootstrapped:4 current:2 must:3 written:1 hypothesize:1 plot:4 drop:1 alone:2 cue:2 leaf:1 guess:31 mcevoy:1 smith:1 short:1 provides:3 math:1 contribute:2 lexicon:5 simpler:1 along:24 prove... |
3,259 | 3,952 | Predictive State Temporal Difference Learning
Geoffrey J. Gordon
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
ggordon@cs.cmu.edu
Byron Boots
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
beb@cs.cmu.edu
Abstract
We propose a new approach to value function a... | 3952 |@word trial:2 version:1 compression:14 instrumental:2 seems:1 open:1 seek:1 simulation:1 covariance:16 decomposition:2 pick:1 harder:1 recursively:1 initial:3 contains:5 series:1 selecting:1 daniel:1 tuned:1 outperforms:2 current:4 comparing:2 must:2 written:1 john:2 ronald:2 informative:3 designed:5 update:1 v:1... |
3,260 | 3,953 | Factorized Latent Spaces with Structured Sparsity
Yangqing Jia1 , Mathieu Salzmann1,2 , and Trevor Darrell1
1
UC Berkeley EECS and ICSI 2 TTI-Chicago
{jiayq,trevor}@eecs.berkeley.edu, salzmann@ttic.edu
Abstract
Recent approaches to multi-view learning have shown that factorizing the information into parts that are sh... | 3953 |@word proceeded:1 private:34 briefly:2 norm:14 nd:15 confirms:1 seek:2 accounting:1 tr:1 reduction:1 contains:3 series:2 salzmann:2 outperforms:2 existing:6 recovered:5 z2:1 chicago:1 remove:2 designed:1 drop:1 generative:5 discovering:1 intelligence:2 phog:10 scotland:3 ith:2 provides:1 detecting:1 location:1 yu... |
3,261 | 3,954 | Learning Kernels with Radiuses of Minimum
Enclosing Balls
Guangyun Chen
Changshui Zhang
Kun Gai
State Key Laboratory on Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Automation, Tsinghua University, Beijing 100084, China
{gaik02, cgy08}@ma... | 3954 |@word repository:2 version:1 eliminating:1 polynomial:1 norm:41 seems:1 unif:3 pick:1 thereby:1 tnlist:1 initial:8 selecting:1 ours:3 bhattacharyya:1 outperforms:6 existing:3 past:1 must:1 john:1 belmont:1 subsequent:1 numerical:2 eleven:1 enables:1 intelligence:1 selected:4 ck2:1 provides:1 preference:3 zhang:1 ... |
3,262 | 3,955 | Epitome driven 3-D Diffusion Tensor image
segmentation: on extracting specific structures?
Kamiya Motwani??
Nagesh Adluru?
?
Computer Sciences
University of Wisconsin
?
Chris Hinrichs??
Andrew Alexander?
Biostatistics & Medical Informatics
University of Wisconsin
{kmotwani,hinrichs,vsingh}@cs.wisc.edu
Vikas Sin... | 3955 |@word mild:1 kohli:1 version:1 briefly:3 polynomial:1 norm:1 seems:1 mri:4 tedious:2 seek:1 rgb:1 bn:1 pick:1 configuration:4 liu:1 zij:8 offering:1 denoting:1 ours:1 existing:2 current:1 contextual:1 anne:1 assigning:2 moo:1 readily:1 must:2 partition:2 j1:2 shape:1 designed:2 atlas:2 medial:1 half:3 pursued:1 s... |
3,263 | 3,956 | Copula Bayesian Networks
Gal Elidan
Department of Statistics
Hebrew University
Jerusalem, 91905, Israel
galel@huji.ac.il
Abstract
We present the Copula Bayesian Network model for representing multivariate
continuous distributions, while taking advantage of the relative ease of estimating univariate distributions. Usi... | 3956 |@word repository:2 middle:2 briefly:3 frigessi:1 cortez:1 open:2 simulation:1 bn:28 decomposition:7 covariance:1 dramatic:1 liu:3 born:1 score:4 contains:1 fragment:1 offering:2 denoting:1 ours:1 nonparanormal:1 existing:2 current:1 comparing:2 elliptical:2 yet:1 dx:2 written:1 portuguese:1 kft:1 explorative:1 as... |
3,264 | 3,957 | Penalized Principal Component Regression on
Graphs for Analysis of Subnetworks
George Michailidis
Department of Statistics and EECS
University of Michigan
Ann Arbor, MI 48109
gmichail@umich.edu
Ali Shojaie
Department of Statistics
University of Michigan
Ann Arbor, MI 48109
shojaie@umich.edu
Abstract
Network models a... | 3957 |@word version:1 proportion:2 tamayo:1 ajj:1 simulation:5 covariance:2 eng:1 moment:1 reduction:8 series:3 selecting:1 denoting:1 longitudinal:1 o2:1 current:1 dupont:1 remove:1 v:1 intelligence:1 selected:3 metabolism:1 discovering:1 signalling:1 ith:3 reciprocal:1 provides:2 node:16 location:1 mathematical:1 alo... |
3,265 | 3,958 | Sample complexity of testing the manifold hypothesis
Hariharan Narayanan?
Laboratory for Information and Decision Systems
EECS, MIT
Cambridge, MA 02139
har@mit.edu
Sanjoy Mitter
Laboratory for Information and Decision Systems
EECS, MIT
Cambridge, MA 02139
mitter@mit.edu
Abstract
The hypothesis that high dimensional d... | 3958 |@word version:3 polynomial:2 nd:1 open:5 d2:1 asks:1 reduction:4 chervonenkis:1 si:5 must:1 additive:3 zeger:1 intelligence:1 devising:1 xk:16 core:1 quantizer:1 mathematical:1 along:2 shatter:2 persistent:1 focs:3 prove:2 fitting:5 expected:3 roughly:1 p1:2 frequently:1 curse:1 cardinality:1 provided:1 bounded:9... |
3,266 | 3,959 | Deterministic Single-Pass Algorithm for LDA
Issei Sato
University of Tokyo, Japan
sato@r.dl.itc.u-tokyo.ac.jp
Kenichi Kurihara
Google
kenichi.kurihara@gmail.com
Hiroshi Nakagawa
University of Tokyo, Japan
n3@dl.itc.u-tokyo.ac.jp
Abstract
We develop a deterministic single-pass algorithm for latent Dirichlet allocati... | 3959 |@word briefly:1 seems:1 nd:3 twelfth:1 series:1 daniel:1 document:59 outperforms:2 existing:2 sugato:1 freitas:1 com:3 nt:6 si:5 gmail:1 attracted:1 bd:2 kdd:1 plot:1 update:52 resampling:2 generative:1 intelligence:2 devising:1 mccallum:2 es:1 short:1 blei:3 provides:1 five:1 mathematical:1 constructed:2 issei:1... |
3,267 | 396 | A VLSI Neural Network for Color Constancy
Andrew Moore
Geoffrey Fox?
Computation and Neural Systems Program, 116-81
Dept. of Physics
California Institute of Technology
California Institute of Technology
Pasadena, CA 91125
Pasadena, CA 91125
John Allman
Dept. of Biology, 216-76
California Institute of Technology
Pasade... | 396 |@word economically:1 middle:1 version:4 disk:1 grey:20 essay:1 simulation:7 sensed:2 rgb:1 minus:3 configuration:2 contains:1 recovered:1 must:1 john:1 realize:1 visible:1 remove:1 designed:1 half:1 caucasian:1 tone:3 plane:4 lamp:1 short:1 colored:9 node:1 location:1 unacceptable:1 constructed:1 direct:1 become:1... |
3,268 | 3,960 | On the Theory of Learning with Privileged
Information
Dmitry Pechyony
NEC Laboratories
Princeton, NJ 08540, USA
pechyony@nec-labs.com
Vladimir Vapnik
NEC Laboratories
Princeton, NJ 08540, USA
vlad@nec-labs.com
Abstract
In Learning Using Privileged Information (LUPI) paradigm, along with the standard training data in ... | 3960 |@word version:9 nd:1 ckd:1 d2:5 contains:4 series:1 existing:2 com:2 dx:20 subsequent:2 realistic:1 v:1 hyperplanes:1 along:1 constructed:3 supply:2 consists:2 indeed:1 provided:1 bounded:4 underlying:1 moreover:2 rn0:1 mass:2 what:2 kind:2 minimizes:6 developed:2 nj:2 ti:5 exactly:2 classifier:2 demonstrates:1 a... |
3,269 | 3,961 | Causal discovery in multiple models from different
experiments
Tom Heskes
Radboud University Nijmegen
The Netherlands
tomh@cs.ru.nl
Tom Claassen
Radboud University Nijmegen
The Netherlands
tomc@cs.ru.nl
Abstract
A long-standing open research problem is how to use information from different
experiments, including bac... | 3961 |@word trial:2 version:1 eliminating:1 proportion:2 nd:2 open:2 closure:1 hyv:1 accounting:1 concise:2 klk:2 carry:1 initial:1 contains:1 uncovered:4 mag:10 outperforms:1 existing:1 current:1 contextual:1 comparing:1 chordal:1 yet:2 must:2 subsequent:2 additive:1 informative:2 enables:1 drop:1 interpretable:2 unsh... |
3,270 | 3,962 | Non-Stochastic Bandit Slate Problems
Satyen Kale
Yahoo! Research
Santa Clara, CA
Lev Reyzin?
Georgia Inst. of Technology
Atlanta, GA
Robert E. Schapire?
Princeton University
Princeton, NJ
skale@yahoo-inc.com
lreyzin@cc.gatech.edu
schapire@cs.princeton.edu
Abstract
We consider bandit problems, motivated by applica... | 3962 |@word version:5 r:4 p0:12 initial:3 cyclic:1 series:1 recovered:1 com:1 current:1 clara:1 must:1 realistic:1 benign:1 update:3 selected:2 warmuth:5 rsk:3 characterization:1 simpler:1 mathematical:2 along:2 shorthand:1 prove:1 manner:1 expected:6 frequently:1 multi:2 little:1 armed:2 considering:1 clicked:1 compet... |
3,271 | 3,963 | Learning Concept Graphs from Text with
Stick-Breaking Priors
Padhraic Smyth
Department of Computer Science
University of California, Irvine
Irvine, CA 92607
smyth@ics.uci.edu
America L. Chambers
Department of Computer Science
University of California, Irvine
Irvine, CA 92697
ahollowa@ics.uci.edu
Mark Steyvers
Depart... | 3963 |@word multitask:1 cnn:1 faculty:1 version:2 nd:3 eng:1 ld:2 reduction:1 initial:5 series:1 uma:1 selecting:1 genetic:3 document:49 existing:6 current:1 yet:3 must:7 enables:1 update:1 generative:8 intelligence:3 leaf:1 yr:4 mccallum:3 ith:2 blei:3 provides:1 boosting:2 node:77 traverse:2 successive:1 org:2 simple... |
3,272 | 3,964 | Double Q-learning
Hado van Hasselt
Multi-agent and Adaptive Computation Group
Centrum Wiskunde & Informatica
Abstract
In some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large
overestimations of action values. These overes... | 3964 |@word mild:1 trial:3 steen:1 polynomial:9 norm:1 tried:1 contraction:1 p0:1 pick:1 boundedness:1 initial:2 contains:1 hasselt:2 comparing:1 nt:7 si:6 dx:9 must:2 skepticism:1 realistic:1 wiewiora:1 update:15 fund:1 greedy:3 leaf:1 weighing:1 selected:1 intelligence:1 ith:1 smith:1 pointer:1 five:1 chakrabarti:1 w... |
3,273 | 3,965 | Network Flow Algorithms for Structured Sparsity
Julien Mairal?
INRIA - Willow Project-Team?
julien.mairal@inria.fr
Rodolphe Jenatton?
INRIA - Willow Project-Team?
rodolphe.jenatton@inria.fr
Guillaume Obozinski
INRIA - Willow Project-Team?
guillaume.obozinski@inria.fr
Francis Bach
INRIA - Willow Project-Team?
franci... | 3965 |@word msr:1 version:2 achievable:1 polynomial:2 norm:36 open:1 grey:1 simulation:1 linearized:1 rgb:1 seek:1 decomposition:3 jacob:1 tr:1 recursively:1 initial:3 contains:7 ecole:1 denoting:1 interestingly:1 existing:2 current:2 com:2 babenko:1 reminiscent:1 dct:4 partition:1 enables:1 remove:1 update:2 selected:... |
3,274 | 3,966 | Stability Approach to Regularization Selection
(StARS) for High Dimensional Graphical Models
Han Liu Kathryn Roeder Larry Wasserman
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
A challenging problem in estimating high-dimensional graphical models is to
choose the regularization parameter in a data-dependen... | 3966 |@word mild:3 version:2 polynomial:1 norm:1 simulation:2 covariance:9 reduction:2 liu:2 contains:2 score:12 selecting:1 series:3 tuned:1 ours:1 nonparanormal:1 outperforms:4 existing:2 affymetrix:1 bradley:1 nicolai:2 surprising:1 must:1 written:1 bd:1 john:2 informative:2 interpretable:1 resampling:1 selected:6 b... |
3,275 | 3,967 | Rescaling, thinning or complementing? On
goodness-of-fit procedures for point process models
and Generalized Linear Models
Wulfram Gerstner
Brain Mind Institute
Ecole Polytechnique F?ed?erale de Lausanne
1015 Lausanne EPFL, Switzerland
wulfram.gerstner@epfl.ch
Felipe Gerhard
Brain Mind Institute
Ecole Polytechnique F?... | 3967 |@word trial:1 version:1 inversion:1 advantageous:1 smirnov:2 nd:1 haslinger:3 unif:2 mimick:1 simulation:3 pipa:2 series:16 exclusively:1 ecole:2 denoting:1 current:1 discretization:2 ka:2 vere:1 readily:1 concatenate:1 happen:1 partition:1 shape:3 enables:1 designed:1 progressively:1 half:1 selected:1 complement... |
3,276 | 3,968 | A Discriminative Latent Model of Image Region and
Object Tag Correspondence
Yang Wang?
Department of Computer Science
University of Illinois at Urbana-Champaign
yangwang@uiuc.edu
Greg Mori
School of Computing Science
Simon Fraser University
mori@cs.sfu.ca
Abstract
We propose a discriminative latent model for annotat... | 3968 |@word seems:1 everingham:1 open:1 d2:1 initial:1 contains:1 zij:31 denoting:1 ours:2 outperforms:2 freitas:1 comparing:1 contextual:1 written:6 partition:1 hofmann:1 shape:1 remove:1 treating:1 plot:1 grass:1 generative:5 fewer:1 advancement:1 half:2 guess:1 intelligence:2 plane:1 blei:2 provides:1 quantized:1 lo... |
3,277 | 3,969 | Structured Determinantal Point Processes
Alex Kulesza
Ben Taskar
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104
{kulesza,taskar}@cis.upenn.edu
Abstract
We present a novel probabilistic model for distributions over sets of structures?
for example, sets of sequences, tre... | 3969 |@word trial:2 determinant:1 middle:3 briefly:1 d2:3 additively:2 decomposition:6 concise:1 mention:1 moment:1 initial:4 contains:2 score:25 selecting:1 ka:4 nt:3 written:1 vere:1 determinantal:18 additive:1 enables:1 treating:1 update:1 clumping:1 v:1 alone:1 intelligence:1 selected:11 leaf:1 item:8 plane:2 yi1:1... |
3,278 | 397 | Integrated Segmentation and Recognition of
Hand-Printed Numerals
James D. Keeler?
David E. Rumelhart
MCC
Psychology Department
3500 W. Balcones Ctr. Dr.
Stanford University
Austin, TX 78759
Stanford, CA 94305
Wee-Kheng Leow
MCC and
University of Texas
Austin, TX 78759
Abstract
Neural network algorithms have proven u... | 397 |@word selforganization:1 willing:1 grey:3 leow:6 contains:1 document:1 com:2 activation:12 yet:1 lang:2 si:4 happen:1 kheng:1 drop:1 plane:1 detecting:1 location:12 sigmoidal:4 height:1 qualitative:1 consists:1 indeed:1 behavior:3 themselves:1 window:1 project:2 underlying:2 what:1 tic:1 kind:1 interpreted:4 every... |
3,279 | 3,970 | Sodium entry efficiency during action potentials: A
novel single-parameter family of Hodgkin-Huxley
models
Renaud Jolivet?
Institute of Pharmacology and Toxicology
University of Z?urich, Z?urich, Switzerland
renaud.jolivet@a3.epfl.ch
Anand Singh
Institute of Pharmacology and Toxicology
University of Z?urich, Z?urich, ... | 3970 |@word proceeded:1 rising:1 hippocampus:1 hyperpolarized:1 open:1 squid:3 cm2:8 pulse:4 simulation:1 initial:1 inefficiency:1 bc:1 interestingly:1 current:31 comparing:1 yet:2 dx:2 physiol:1 half:1 parameterization:2 hodgkinhuxley:2 provides:1 gx:2 magistretti:2 height:1 ik:9 jonas:1 prove:1 introduce:2 indeed:2 b... |
3,280 | 3,971 | A POMDP Extension with Belief-dependent Rewards
Mauricio Araya-L?opez
Olivier Buffet
Vincent Thomas
Franc?ois Charpillet
Nancy Universit?e / INRIA
LORIA ? Campus Scientifique ? BP 239
54506 Vandoeuvre-l`es-Nancy Cedex ? France
firstname.lastname@loria.fr
Abstract
Partially Observable Markov Decision Processes (PO... | 3971 |@word h:1 mild:1 version:1 norm:6 open:1 bn:3 contraction:1 pick:2 mention:1 recursively:1 initial:3 series:1 past:1 existing:1 current:5 discretization:1 yet:5 must:2 written:1 numerical:1 hsvi2:1 remove:1 update:15 intelligence:5 selected:3 guess:1 advancement:1 smith:2 lr:1 provides:1 math:1 location:1 hyperpl... |
3,281 | 3,972 | Random Walk Approach to Regret Minimization
Hariharan Narayanan
MIT
Cambridge, MA 02139
har@mit.edu
Alexander Rakhlin
University of Pennsylvania
Philadelphia, PA 19104
rakhlin@wharton.upenn.edu
Abstract
We propose a computationally efficient random walk on a convex body which
rapidly mixes to a time-varying Gibbs dis... | 3972 |@word multitask:2 polynomial:5 seems:1 norm:7 d2:2 additively:1 forecaster:5 linearized:2 covariance:3 recursively:1 reduction:5 initial:2 contains:1 existing:1 current:1 discretization:2 dikin:8 yet:2 dx:6 intriguing:1 written:1 additive:1 partition:2 shape:2 stationary:4 congestion:1 warmuth:2 provides:1 math:1... |
3,282 | 3,973 | Scrambled Objects for Least-Squares Regression
Odalric-Ambrym Maillard and R?emi Munos
SequeL Project, INRIA Lille - Nord Europe, France
{odalric.maillard, remi.munos}@inria.fr
Abstract
We consider least-squares regression using a randomly generated subspace GP ?
F of finite dimension P , where F is a function space ... | 3973 |@word version:1 briefly:1 middle:2 norm:8 seems:2 inversion:1 tedious:1 hu:1 egp:3 covariance:1 harder:1 moment:3 initial:17 series:1 rkhs:5 janson:1 current:1 surprising:1 john:2 griebel:2 numerical:10 ronald:1 enables:5 plot:6 greedy:2 vanishing:4 num:1 characterization:2 provides:4 bijection:1 node:1 math:1 c6... |
3,283 | 3,974 | A primal-dual algorithm for group sparse
regularization with overlapping groups
Silvia Villa
DISI- Universit`a di Genova
villa@dima.unige.it
Sofia Mosci
DISI- Universit`a di Genova
mosci@disi.unige.it
Lorenzo Rosasco
IIT - MIT
lrosasco@MIT.EDU
Alessandro Verri
DISI- Universit`a di Genova
verri@disi.unige.it
Abstra... | 3974 |@word norm:5 c0:3 semicontinuous:1 simulation:1 decomposition:2 jacob:1 mention:1 thereby:1 minus:1 tr:1 reduction:2 series:3 contains:1 selecting:1 nesta:1 comparing:2 must:5 additive:1 numerical:4 enables:1 interpretable:1 update:2 v:1 selected:5 steepest:1 core:1 provides:2 math:2 zhang:1 dn:2 become:1 replica... |
3,284 | 3,975 | Empirical Risk Minimization
with Approximations of Probabilistic Grammars
Noah A. Smith
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
nasmith@cs.cmu.edu
Shay B. Cohen
Language Technologies Institute
School of Computer Science
Carnegie Mellon University... | 3975 |@word mild:1 polynomial:2 km:8 boundedness:3 recursively:1 charniak:1 interestingly:1 dx:7 reminiscent:1 parsing:7 must:1 fn:34 implying:2 generative:1 reranking:1 discovering:1 warmuth:2 ith:1 smith:3 lr:1 characterization:1 coarse:2 unbounded:1 constructed:1 become:1 competitiveness:1 consists:1 dan:1 interscie... |
3,285 | 3,976 | Sparse Instrumental Variables (SPIV) for
Genome-Wide Studies
Felix V. Agakov
Public Health Sciences
University of Edinburgh
felixa@aivalley.com
Paul McKeigue
Public Health Sciences
University of Edinburgh
paul.mckeigue@ed.ac.uk
Jon Krohn
WTCHG, Oxford
jon.krohn@magd.ox.ac.uk
Amos Storkey
School of Informatics
Univer... | 3976 |@word trial:3 determinant:1 manageable:2 instrumental:18 stronger:3 loading:1 proportion:1 sex:5 heuristically:1 integrative:1 simulation:1 covariance:6 accounting:1 score:8 selecting:1 genetic:26 recovered:1 com:1 comparing:4 z2:1 activation:1 yet:1 subsequent:2 additive:1 wx:1 remove:1 plot:3 update:1 aside:1 d... |
3,286 | 3,977 | Learning from Logged Implicit Exploration Data
Alexander L. Strehl ?
Facebook Inc.
1601 S California Ave
Palo Alto, CA 94304
astrehl@facebook.com
John Langford
Yahoo! Research
111 West 40th Street, 9th Floor
New York, NY, USA 10018
jl@yahoo-inc.com
Sham M. Kakade
Department of Statistics
University of Pennsylvania
Ph... | 3977 |@word mild:1 eliminating:1 achievable:1 proportion:1 unif:5 seek:1 simulation:1 harder:1 moment:3 contains:5 score:4 selecting:1 horvitz:1 existing:2 current:2 com:3 contextual:12 clara:1 yet:1 chu:2 must:2 john:5 plot:1 implying:1 greedy:2 provides:3 contribute:1 simpler:1 evaluator:5 zhang:1 unbounded:1 become:... |
3,287 | 3,978 | Worst-case bounds on the quality of max-product
fixed-points
? Cerquides
Jesus
Artificial Intelligence Research Institute (IIIA)
Spanish Scientific Research Council (CSIC)
Campus UAB, Bellaterra, Spain
cerquide@iiia.csic.es
Meritxell Vinyals
Artificial Intelligence Research Institute (IIIA)
Spanish Scientific Researc... | 3978 |@word stronger:1 verona:2 consolider:1 adrian:1 confirms:1 atul:1 simplifying:1 configuration:4 contains:6 hereafter:1 karger:1 daniel:1 com:1 assigning:1 guez:1 refines:1 partition:2 koetter:2 christian:1 remove:2 plot:3 update:2 intelligence:4 fewer:2 device:1 xk:2 provides:4 characterization:2 node:4 direct:1 ... |
3,288 | 3,979 | Sphere Embedding:
An Application to Part-of-Speech Induction
Yariv Maron
Gonda Brain Research Center
Bar-Ilan University
Ramat-Gan 52900, Israel
syarivm@yahoo.com
Michael Lamar
Department of Mathematics and Computer Science
Saint Louis University
St. Louis, MO 63103, USA
mlamar@slu.edu
Elie Bienenstock
Division of A... | 3979 |@word multitask:1 faculty:1 version:2 bigram:14 proportion:2 heuristically:1 contrastive:1 thereby:1 reduction:3 initial:1 score:8 past:3 outperforms:1 com:1 od:1 comparing:2 assigning:3 yet:1 john:2 numerical:1 partition:11 ronan:1 update:10 v:1 generative:1 website:2 parameterization:1 amir:1 desktop:1 warmuth:... |
3,289 | 398 | A Lagrangian Approach to Fixed Points
Eric Mjolsness
Department of Computer Science
Yale University
P.O. Box 2158 Yale Station
New Haven, CT 16520-2158
Willard L. Miranker
IBM Watson Research Center
Yorktown Heights, NY 10598
Abstract
We present a new way to derive dissipative, optimizing dynamics from
the Lagrangia... | 398 |@word h:1 effect:1 version:1 involves:1 indicate:1 implies:1 objective:9 simulation:4 linearized:1 usual:1 during:1 virtual:6 exchange:1 yorktown:1 oc:1 yaleu:1 criterion:1 simulated:1 lagrangians:2 preliminary:1 demonstrate:1 adjusted:1 motion:1 pl:1 current:2 ka:1 index:1 relationship:1 ratio:2 novel:2 dx:1 must... |
3,290 | 3,980 | Short-term memory in neuronal networks through
dynamical compressed sensing
Surya Ganguli
Sloan-Swartz Center for Theoretical Neurobiology, UCSF, San Francisco, CA 94143
surya@phy.ucsf.edu
Haim Sompolinsky
Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel
and Center for Brain ... | 3980 |@word trial:1 norm:4 stronger:1 disk:1 pulse:3 simulation:6 covariance:1 pg:9 q1:4 dramatic:1 thereby:5 solid:1 initial:2 substitution:1 phy:1 mag:1 daniel:1 past:18 existing:2 imaginary:1 current:4 recovered:1 si:2 yet:1 intriguing:1 must:2 additive:1 numerical:2 wx:1 distant:1 analytic:1 remove:1 plot:1 fund:1 ... |
3,291 | 3,981 | Exact learning curves for Gaussian process regression
on large random graphs
Peter Sollich
Department of Mathematics
King?s College London
London, WC2R 2LS, U.K.
peter.sollich@kcl.ac.uk
Matthew J. Urry
Department of Mathematics
King?s College London
London, WC2R 2LS, U.K.
matthew.urry@kcl.ac.uk
Abstract
We study lea... | 3981 |@word kong:1 briefly:1 version:2 kondor:1 nd:1 c0:3 twelfth:1 open:1 simulation:5 covariance:20 datagenerating:1 solid:2 recursively:1 electronics:1 initial:1 contains:3 outperforms:1 current:1 comparing:2 analysed:1 yet:1 written:3 numerical:1 partition:4 update:6 intelligence:1 warmuth:1 normalising:1 character... |
3,292 | 3,982 | A biologically plausible network for the computation
of orientation dominance
Nuno Vasconcelos
Statistical Visual Computing Laboratory
University of California San Diego
La Jolla, CA 92039
nuno@ece.ucsd.edu
Kritika Muralidharan
Statistical Visual Computing Laboratory
University of California San Diego
La Jolla, CA 920... | 3982 |@word neurophysiology:2 trial:1 cox:1 version:3 dalal:1 wiesel:1 seems:1 triggs:1 twelfth:2 decomposition:1 q1:1 shechtman:1 reduction:1 contains:3 efficacy:1 exclusively:1 tuned:2 interestingly:1 suppressing:2 past:2 outperforms:3 current:2 comparing:1 surprising:2 activation:1 dx:2 written:1 informative:1 shape... |
3,293 | 3,983 | Fractionally Predictive Spiking Neurons
Jaldert O. Rombouts
CWI, Life Sciences
Amsterdam, The Netherlands
J.O.Rombouts@cwi.nl
Sander M. Bohte
CWI, Life Sciences
Amsterdam, The Netherlands
S.M.Bohte@cwi.nl
Abstract
Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional d... | 3983 |@word neurophysiology:2 version:1 rising:1 stronger:2 seems:2 calculus:1 simulation:1 seek:1 electrosensory:1 amply:1 incurs:1 minus:1 carry:3 initial:4 contains:1 efficacy:2 series:2 past:7 existing:1 current:10 activation:3 plot:1 aside:1 v:1 half:2 greedy:2 leaf:1 fewer:1 smith:2 short:2 filtered:1 contribute:... |
3,294 | 3,984 | Fast global convergence of gradient methods
for high-dimensional statistical recovery
Alekh Agarwal1
Sahand N. Negahban1
Martin J. Wainwright1,2
1
Department of Electrical Engineering and Computer Science and Department of Statistics2
University of California, Berkeley
Berkeley, CA 94720-1776
{alekh,sahand n,wainwrig}... | 3984 |@word multitask:2 trial:2 version:9 polynomial:1 norm:16 c0:3 suitably:1 simulation:2 contraction:2 covariance:2 thereby:1 harder:1 reduction:1 series:3 nesta:1 rkhs:1 past:1 wainwrig:1 existing:1 written:1 additive:1 plot:5 update:3 progressively:1 v:5 greedy:1 selected:1 core:1 iterates:7 provides:1 simpler:1 z... |
3,295 | 3,985 | Multiple Kernel Learning and the SMO Algorithm
S. V. N. Vishwanathan, Zhaonan Sun, Nawanol Theera-Ampornpunt
Purdue University
vishy@stat.purdue.edu, sunz@stat.purdue.edu, ntheeraa@cs.purdue.edu
Manik Varma
Microsoft Research India
manik@microsoft.com
Abstract
Our objective is to train p-norm Multiple Kernel Learning... | 3985 |@word briefly:1 polynomial:3 norm:23 d2:1 tried:3 decomposition:1 q1:1 d0k:3 thereby:1 harder:1 carry:2 initial:1 wrapper:2 interestingly:1 recovered:1 com:2 readily:1 eleven:1 analytic:1 plot:1 half:6 selected:6 fewer:1 greedy:1 warmuth:1 beginning:1 core:5 pointer:1 accessed:1 five:1 along:1 direct:1 shooting:1... |
3,296 | 3,986 | Optimal Web-scale Tiering as a Flow Problem
Novi Quadrianto
SML-NICTA & RSISE-ANU
Canberra, ACT, Australia
novi.quad@gmail.com
Gilbert Leung
eBay, Inc.
San Jose, CA, USA
gleung@alum.mit.edu
Kostas Tsioutsiouliklis
Yahoo! Labs
Sunnyvale, CA, USA
kostas@yahoo-inc.com
Alexander J. Smola
Yahoo! Research
Santa Clara, CA... | 3986 |@word middle:2 version:6 compression:1 advantageous:1 disk:1 eng:1 p0:2 incurs:1 carry:1 reduction:3 contains:2 score:2 efficacy:1 document:35 outperforms:1 current:1 com:2 clara:1 gmail:1 assigning:1 yet:1 readily:3 written:1 numerical:1 remove:1 drop:1 treating:1 update:12 n0:2 alone:1 fewer:1 nq:1 desktop:1 co... |
3,297 | 3,987 | Natural Policy Gradient Methods with
Parameter-based Exploration for Control Tasks
Atsushi Miyamae?? , Yuichi Nagata? , Isao Ono? , Shigenobu Kobayashi?
?: Department of Computational Intelligence and Systems Science
Tokyo Institute of Technology, Kanagawa, Japan
?: Research Fellow of the Japan Society for the Promotio... | 3987 |@word mild:1 trial:2 version:2 seek:5 covariance:5 decomposition:2 carry:1 reduction:2 efficacy:1 genetic:1 outperforms:2 o2:2 current:6 yet:1 realize:1 ronald:1 numerical:1 fn:1 realistic:2 motor:4 plot:1 update:2 sehnke:3 stationary:1 intelligence:3 prohibitive:1 fewer:1 parameterization:1 steepest:2 premultipl... |
3,298 | 3,988 | Efficient and Robust Feature Selection via Joint
`2,1-Norms Minimization
Feiping Nie
Computer Science and Engineering
University of Texas at Arlington
feipingnie@gmail.com
Heng Huang
Computer Science and Engineering
University of Texas at Arlington
heng@uta.edu
Xiao Cai
Computer Science and Engineering
University of... | 3988 |@word norm:50 seems:1 tamayo:1 motoda:1 elisseeff:1 reduction:1 wrapper:4 liu:2 contains:6 score:4 selecting:1 series:1 past:1 outperforms:1 existing:2 ka:5 com:1 current:2 bradley:1 comparing:1 luo:1 gmail:1 yet:1 written:3 readily:1 john:1 benign:2 eleven:1 remove:1 designed:2 intelligence:2 selected:7 beginnin... |
3,299 | 3,989 | Mixture of time -warped trajectory models for
movement decoding
Elaine A. Corbett, Eric J. Perreault and Konrad P. K?rding
Northwestern University
Chicago, IL 60611
ecorbett@u.northwestern.edu
Abstract
Applications of Brain-Machine-Interfaces typically estimate user intent
based on biological signals that are under v... | 3989 |@word neurophysiology:2 middle:1 seems:2 johansson:1 approved:1 tried:1 covariance:3 accounting:1 dramatic:2 tr:2 recursively:1 reduction:1 initial:1 interestingly:2 current:1 si:1 assigning:2 must:1 kft:13 john:1 chicago:1 visible:1 additive:1 realistic:1 wanted:4 motor:4 treating:1 designed:1 stationary:1 gener... |
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