Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
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3,000 | 3,719 | Online Learning of Assignments
Matthew Streeter
Daniel Golovin
Andreas Krause
Google, Inc.
Pittsburgh, PA 15213
mstreeter@google.com
Carnegie Mellon University
Pittsburgh, PA 15213
dgolovin@cs.cmu.edu
California Institute of Technology
Pasadena, CA 91125
krausea@caltech.edu
Abstract
Which ads should we display i... | 3719 |@word trial:1 exploitation:1 version:4 polynomial:2 stronger:2 laurence:2 c0:2 willing:1 pick:1 accommodate:1 liu:1 contains:1 selecting:6 daniel:4 ours:2 outperforms:1 com:1 comparing:1 yet:1 assigning:1 must:4 additive:4 happen:1 partition:6 informative:2 kdd:1 sponsored:11 ligett:1 half:5 selected:3 greedy:18 ... |
3,001 | 372 | A Recurrent Neural Network for Word Identification
from Continuous Phoneme Strings
Candace A. Kamm
Bellcore
Morristown, NJ 07962-1910
Robert B. Allen
Bellcore
Morristown, NJ 07962-1910
Abstract
A neural network architecture was designed for locating word boundaries and
identifying words from phoneme sequences. This a... | 372 |@word proportion:2 accounting:1 initial:1 substitution:3 series:1 contains:1 past:5 current:3 comparing:1 surprising:1 activation:30 si:2 lang:1 realistic:2 subsequent:1 webster:2 designed:3 drop:1 update:1 selected:4 fewer:3 lexicon:1 location:5 constructed:1 incorrect:1 consists:2 prove:1 expected:2 elman:2 kamm... |
3,002 | 3,720 | A Bayesian Analysis of Dynamics in Free Recall
Richard Socher
Department of Computer Science
Stanford University
Stanford, CA 94305
richard@socher.org
Samuel J. Gershman, Adler J. Perotte, Per B. Sederberg
Department of Psychology
Princeton University
Princeton, NJ 08540
{sjgershm,aperotte,persed}@princeton.edu
Kenne... | 3720 |@word h:2 trial:2 illustrating:1 middle:1 proportion:4 open:1 heuristically:1 simulation:5 r:2 thereby:2 initial:1 series:3 exclusively:4 document:14 outperforms:1 existing:1 freitas:1 current:5 comparing:3 contextual:3 remove:1 plot:6 designed:1 v:1 cue:2 generative:6 half:1 item:22 es:1 sederberg:6 core:1 recor... |
3,003 | 3,721 | Noisy Generalized Binary Search
Robert Nowak
University of Wisconsin-Madison
1415 Engineering Drive, Madison WI 53706
nowak@ece.wisc.edu
Abstract
This paper addresses the problem of noisy Generalized Binary Search (GBS).
GBS is a well-known greedy algorithm for determining a binary-valued hypothesis through a sequenc... | 3721 |@word version:5 polynomial:1 proportion:2 norm:1 c0:2 open:1 p0:7 mention:1 moment:1 initial:1 selecting:3 past:2 existing:1 must:4 partition:2 informative:3 ainen:1 update:5 n0:3 greedy:4 selected:13 leaf:1 devising:1 half:1 plane:1 beginning:1 reciprocal:1 prespecified:1 characterization:2 provides:1 revisited:... |
3,004 | 3,722 | Bootstrapping from Game Tree Search
Joel Veness
University of NSW and NICTA
Sydney, NSW, Australia 2052
joelv@cse.unsw.edu.au
David Silver
University of Alberta
Edmonton, AB Canada T6G2E8
silver@cs.ualberta.ca
William Uther
NICTA and the University of NSW
Sydney, NSW, Australia 2052
William.Uther@nicta.com.au
Alan ... | 3722 |@word version:2 stronger:1 simulation:1 propagate:1 invoking:1 nsw:6 recursively:1 carry:1 ld:3 initial:4 configuration:1 series:2 score:1 contains:1 tuned:1 cleared:1 existing:1 current:3 com:1 must:3 bd:5 subsequent:8 update:18 alone:2 intelligence:4 leaf:24 selected:2 smith:2 transposition:12 cse:2 node:21 con... |
3,005 | 3,723 | Anomaly Detection with Score functions based on
Nearest Neighbor Graphs
Manqi Zhao
ECE Dept.
Boston University
Boston, MA 02215
mqzhao@bu.edu
Venkatesh Saligrama
ECE Dept.
Boston University
Boston, MA, 02215
srv@bu.edu
Abstract
We propose a novel non-parametric adaptive anomaly detection algorithm for high
dimension... | 3723 |@word trial:1 repository:2 middle:2 nd:1 open:1 cm2:1 r:7 simulation:1 p0:6 pick:1 solid:1 reduction:2 contains:1 score:35 efficacy:1 denoting:1 comparing:1 ida:1 skipping:1 yet:1 must:1 written:1 mst:3 plot:1 v:4 greedy:1 mpm:1 dissertation:1 detecting:2 provides:1 characterization:2 node:2 postal:1 mcdiarmid:2 ... |
3,006 | 3,724 | Unsupervised Feature Selection for the
k-means Clustering Problem
Christos Boutsidis
Department of Computer Science
Rensselaer Polytechnic Institute
Troy, NY 12180
boutsc@cs.rpi.edu
Michael W. Mahoney
Department of Mathematics
Stanford University
Stanford, CA 94305
mmahoney@cs.stanford.edu
Petros Drineas
Department ... | 3724 |@word trial:4 version:1 briefly:1 norm:13 seems:1 stronger:1 c0:1 sammon:1 seek:2 decomposition:4 elisseeff:1 euclidian:1 score:20 exclusively:1 denoting:2 document:9 existing:1 atlantic:1 current:1 rpi:4 universality:1 john:2 partition:11 kdd:1 seeding:2 v:1 selected:9 provides:1 boosting:1 zhang:1 mathematical:... |
3,007 | 3,725 | Bayesian Belief Polarization
Alan Jern
Department of Psychology
Carnegie Mellon University
ajern@cmu.edu
Kai-min K. Chang
Language Technologies Institute
Carnegie Mellon University
kkchang@cs.cmu.edu
Charles Kemp
Department of Psychology
Carnegie Mellon University
ckemp@cmu.edu
Abstract
Empirical studies have docum... | 3725 |@word trial:6 middle:1 version:1 stronger:2 proportion:4 seems:1 norm:1 d2:2 confirms:1 simulation:15 detective:1 solid:2 series:1 score:6 document:1 petty:1 existing:2 recovered:1 culprit:1 follower:2 must:3 applicant:1 cpds:13 shape:1 christian:6 plot:2 update:5 generative:1 selected:1 directory:5 beginning:2 m... |
3,008 | 3,726 | Extending Phase Mechanism to Differential
Motion Opponency for Motion Pop-Out
Yicong Meng and Bertram E. Shi
Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
{eeyicong, eebert}@ust.hk
Abstract
We extend the concept of phase tuning, a u... | 3726 |@word neurophysiology:2 kong:2 middle:2 integrative:2 simulation:2 rhesus:1 covariance:1 reduction:1 born:2 disparity:12 tuned:10 imaginary:7 bradley:1 contextual:3 nt:1 ust:1 mst:2 medial:1 cue:1 half:1 location:10 along:2 constructed:1 differential:20 brain:1 inspired:2 freeman:1 underlying:1 didn:2 monkey:2 di... |
3,009 | 3,727 | Th e Wi sdo m o f Cro wds in th e Recoll ection o f
Ord er In fo rma ti on
Mark Steyvers, Michael Lee, Brent Miller, Pernille Hemmer
Department of Cognitive Sciences
University of California Irvine
mark.steyvers@uci.edu
Abstract
When individuals independently recollect events or retrieve facts from
memory, how can we... | 3727 |@word trial:2 middle:1 judgement:1 proportion:1 seems:3 covariance:1 pick:1 solid:1 initial:1 series:1 score:2 united:1 bc:4 interestingly:1 franklin:2 outperforms:2 existing:1 ka:1 nt:2 surprising:1 si:1 john:2 numerical:1 designed:1 alone:1 intelligence:1 guess:2 item:36 cult:1 mental:2 provides:1 location:5 pr... |
3,010 | 3,728 | Canonical Time Warping
for Alignment of Human Behavior
Fernando de la Torre
Robotics Institute
Carnegie Mellon University
ftorre@cs.cmu.edu
Feng Zhou
Robotics Institute
Carnegie Mellon University
www.f-zhou.com
Abstract
Alignment of time series is an important problem to solve in many scientific disciplines. In part... | 3728 |@word illustrating:1 pw:12 norm:1 replicate:1 covariance:1 pick:1 tr:1 accommodate:2 recursively:1 reduction:1 initial:1 configuration:1 series:17 contains:1 liu:1 com:1 dx:11 kdb:1 realistic:1 numerical:1 wx:14 enables:1 xdx:2 selected:3 ith:3 provides:2 node:1 toronto:1 successive:1 compressible:1 revisited:1 a... |
3,011 | 3,729 | Nonparametric Bayesian Texture Learning and
Synthesis
Long (Leo) Zhu1 Yuanhao Chen2 William Freeman1 Antonio Torralba1
1
2
CSAIL, MIT
Department of Statistics, UCLA
{leozhu, billf, antonio}@csail.mit.edu
yhchen@stat.ucla.edu
Abstract
We present a nonparametric Bayesian method for texture learning and synthesis.
A text... | 3729 |@word inpainting:2 liu:3 contains:1 selecting:2 tuned:2 existing:1 z2:1 zhu1:1 shape:2 analytic:1 remove:2 designed:2 generative:2 discovering:2 instantiate:1 nq:1 cue:1 dhmm:36 colored:1 blei:1 provides:1 node:24 location:2 simpler:3 become:1 inspired:1 globally:1 freeman:1 automatically:5 cpu:1 becomes:4 begin:... |
3,012 | 373 | Sequential Adaptation of Radial Basis Function
Neural Networks and its Application to
Time-series Prediction
v.
Kadirkamanathan
Engineering Department
Cambridge University
Cambridge CB2 IPZ, UK
M. Niranjan
F. Fallside
Abstract
We develop a sequential adaptation algorithm for radial basis function
(RBF) neural net... | 373 |@word indicate:2 norm:4 evolution:1 direction:1 hence:4 analytically:1 farber:3 memoryless:2 laboratory:2 kadirkamanathan:11 stochastic:10 centered:1 white:2 usual:1 diagonal:1 fallside:5 in_:1 implementing:1 tr:3 eqns:1 gradient:2 subspace:2 distance:1 reduction:1 initial:2 argmjn:1 series:16 investigation:1 evid... |
3,013 | 3,730 | Streaming Pointwise Mutual Information
Ashwin Lall
Georgia Institute of Technology
Atlanta, GA 30332, USA
Benjamin Van Durme
University of Rochester
Rochester, NY 14627, USA
Abstract
Recent work has led to the ability to perform space efficient, approximate counting
over large vocabularies in a streaming context. Mo... | 3730 |@word h:1 trial:1 version:2 bigram:1 seems:1 norm:1 simplifying:1 initial:2 contains:1 score:5 exclusively:1 denoting:1 document:14 prefix:1 current:2 comparing:1 crawling:1 must:3 written:1 john:1 ronald:1 happen:1 remove:1 designed:1 displace:3 update:6 drop:1 hash:1 cue:7 half:4 item:9 recompute:1 accessed:1 t... |
3,014 | 3,731 | Predicting the Optimal Spacing of Study:
A Multiscale Context Model of Memory
Michael C. Mozer? , Harold Pashler? , Nicholas Cepeda? ,
Robert Lindsey? , & Ed Vul?
?
Dept. of Computer Science, University of Colorado
?
Dept. of Psychology, UCSD
?
Dept. of Psychology, York University
?
Dept. of Brain and Cognitive Scienc... | 3731 |@word trial:2 advantageous:1 proportion:2 open:1 simulation:2 tried:2 pick:1 incurs:1 solid:5 contains:1 interestingly:1 existing:3 current:2 contextual:10 surprising:1 activation:8 yet:1 si:8 must:1 intriguing:2 shape:2 drop:1 designed:1 update:14 cue:1 devising:1 item:36 short:4 core:2 pointer:1 provides:3 char... |
3,015 | 3,732 | On Invariance in Hierarchical Models
Jake Bouvrie, Lorenzo Rosasco, and Tomaso Poggio
Center for Biological and Computational Learning
Massachusetts Institute of Technology
Cambridge, MA USA
{jvb,lrosasco}@mit.edu, tp@ai.mit.edu
Abstract
A goal of central importance in the study of hierarchical models for object recog... | 3732 |@word mild:1 illustrating:2 wiesel:2 norm:1 km:1 hu:3 confirms:2 homomorphism:1 concise:3 thereby:2 tr:1 initial:3 atb:3 contains:1 cyclic:3 document:1 past:1 o2:9 recovered:1 imat:1 must:7 written:2 readily:1 stemming:1 shape:1 discrimination:2 intelligence:1 leaf:1 guess:1 plane:4 core:1 characterization:1 prov... |
3,016 | 3,733 | Code-specific policy gradient rules for
spiking neurons
Henning Sprekeler? Guillaume Hennequin Wulfram Gerstner
Laboratory for Computational Neuroscience
?
Ecole
Polytechnique F?ed?erale de Lausanne
1015 Lausanne
Abstract
Although it is widely believed that reinforcement learning is a suitable tool for
describing beh... | 3733 |@word trial:14 version:3 advantageous:1 open:1 simulation:6 versatile:1 reduction:1 ecole:1 interestingly:1 suppressing:2 past:1 current:3 discretization:1 skipping:1 yet:1 reminiscent:1 plasticity:11 shape:5 alone:4 generative:1 intelligence:1 vanishing:1 filtered:1 colored:1 behavioral:1 introduce:1 indeed:1 ex... |
3,017 | 3,734 | The Ordered Residual Kernel for
Robust Motion Subspace Clustering
Tat-Jun Chin, Hanzi Wang and David Suter
School of Computer Science
The University of Adelaide, South Australia
{tjchin, hwang, dsuter}@cs.adelaide.edu.au
Abstract
We present a novel and highly effective approach for multi-body motion segmentation. Dra... | 3734 |@word private:1 version:1 compression:2 polynomial:1 norm:5 km:11 zelnik:1 tat:1 seek:1 bn:1 decomposition:2 reduction:4 series:1 contains:1 seriously:1 rkhs:6 okayama:1 outperforms:3 recovered:1 surprising:1 scatter:1 realize:1 subsequent:3 shape:2 remove:1 core:2 gpca:10 equi:1 location:1 firstly:1 zhang:1 dn:1... |
3,018 | 3,735 | Adaptive Design Optimization in Experiments with
People
Daniel R. Cavagnaro
Department of Psychology
Ohio State University
cavagnaro.2@osu.edu
Mark A. Pitt
Department of Psychology
Ohio State University
pitt.2@osu.edu
Jay I. Myung
Department of Psychology
Ohio State University
myung.1@osu.edu
Abstract
In cognitive ... | 3735 |@word neurophysiology:1 trial:7 proportion:3 replicate:1 stronger:1 bf:2 nd:1 open:1 simulation:4 p0:2 moment:1 reduction:2 series:3 contains:1 daniel:1 denoting:1 subjective:1 current:7 comparing:4 surprising:2 must:2 john:1 visible:1 numerical:1 informative:6 drop:1 designed:2 plot:3 discrimination:6 alone:1 le... |
3,019 | 3,736 | Fast Learning from Non-i.i.d. Observations
Ingo Steinwart
Information Sciences Group CCS-3
Los Alamos National Laboratory
Los Alamos, NM 87545, USA
ingo@lanl.gov
Andreas Christmann
University of Bayreuth
Department of Mathematics
D-95440 Bayreuth
Andreas.Christmann@uni-bayreuth.de
Abstract
We prove an oracle inequal... | 3736 |@word mild:1 version:1 briefly:3 polynomial:1 stronger:2 norm:1 nd:2 suitably:1 bn:1 pick:2 series:6 rkhs:2 ours:1 past:1 bradley:1 scovel:2 surprising:1 fn:1 numerical:1 subsequent:1 n0:2 stationary:4 accepting:1 provides:1 boosting:5 math:2 dn:38 prove:3 khk:1 indeed:1 behavior:1 growing:1 inspired:1 gov:1 cons... |
3,020 | 3,737 | Construction of Nonparametric Bayesian Models
from Parametric Bayes Equations
Peter Orbanz
University of Cambridge and ETH Zurich
p.orbanz@eng.cam.ac.uk
Abstract
We consider the general problem of constructing nonparametric Bayesian models
on infinite-dimensional random objects, such as functions, infinite graphs or ... | 3737 |@word h:1 version:4 dalal:1 stronger:1 heuristically:1 closure:2 eng:1 covariance:1 simplifying:1 commute:2 accommodate:1 carry:4 contains:2 existing:2 si:4 assigning:1 must:2 partition:1 intelligence:1 selected:1 item:5 accordingly:2 smith:1 transposition:1 blei:1 coarse:1 provides:1 bijection:1 readability:1 pr... |
3,021 | 3,738 | A Fast, Consistent Kernel Two-Sample Test
Kenji Fukumizu
Inst. of Statistical Mathematics
Tokyo Japan
fukumizu@ism.ac.jp
Arthur Gretton
Carnegie Mellon University
MPI for Biological Cybernetics
arthur.gretton@gmail.com
Bharath K. Sriperumbudur
Dept. of ECE, UCSD
La Jolla, CA 92037
bharathsv@ucsd.edu
Zaid Harchaoui
... | 3738 |@word neurophysiology:1 version:1 briefly:1 norm:1 smirnov:2 d2:2 simulation:2 covariance:6 tr:7 reduction:1 moment:8 fragment:1 rkhs:7 kurt:1 fa8750:1 com:2 gmail:2 yet:2 must:2 w911nf0810242:1 john:1 visible:1 happen:1 zaid:2 plot:1 resampling:3 v:6 mvar:1 spec:22 recherche:1 eskin:1 math:1 five:2 mathematical:... |
3,022 | 3,739 | Entropic Graph Regularization in Non-Parametric
Semi-Supervised Classification
Amarnag Subramanya & Jeff Bilmes
Department of Electrical Engineering, University of Washington, Seattle.
{asubram,bilmes}@ee.washington.edu
Abstract
We prove certain theoretical properties of a graph-regularized transductive learning obje... | 3739 |@word briefly:1 version:2 nd:1 glue:1 tedious:1 disk:1 bn:1 covariance:1 independant:1 q1:1 solid:1 ipm:1 series:3 score:1 tuned:3 document:1 outperforms:3 current:2 si:1 must:2 john:1 numerical:1 partition:3 treating:1 update:9 v:6 alone:1 intelligence:3 selected:1 core:5 node:29 successive:3 unbounded:1 windowe... |
3,023 | 374 | Statistical Mechanics of Temporal Association
in Neural Networks with Delayed Interactions
Andreas V.M. Herz
Division of Chemistry
Caltech 139-74
Pasadena, CA 91125
Zhaoping Li
School of Natural Sciences
Institute for Advanced Study
Princeton, NJ 08540
J. Leo van Hemmen
Physik-Department
der TU M iinchen
D-8046 Garc... | 374 |@word version:1 seems:2 nd:1 suitably:1 jijsj:1 physik:1 simulation:4 cyclic:2 efficacy:5 ala:1 recovered:1 nt:1 si:17 written:1 john:1 numerical:3 plasticity:1 analytic:2 eab:1 stationary:1 intelligence:1 twostate:1 accordingly:1 eba:1 hamiltonian:2 math:1 direct:1 differential:1 prove:2 consists:1 manner:1 intro... |
3,024 | 3,740 | Time-rescaling methods for the estimation and
assessment of non-Poisson neural encoding models
Jonathan W. Pillow
Departments of Psychology and Neurobiology
University of Texas at Austin
pillow@mail.utexas.edu
Abstract
Recent work on the statistical modeling of neural responses has focused on modulated renewal proces... | 3740 |@word neurophysiology:1 version:1 middle:4 polynomial:1 proportion:2 smirnov:1 nd:1 bf:1 d2:1 jacob:1 carry:3 score:2 daniel:1 ording:1 outperforms:1 ka:2 comparing:1 current:1 scatter:1 written:1 must:1 additive:1 alam:1 interspike:7 shape:2 motor:1 plot:5 implying:1 nervous:1 inspection:2 ith:1 coleman:1 provid... |
3,025 | 3,741 | Semi-supervised Regression using Hessian Energy
with an Application to Semi-supervised
Dimensionality Reduction
Kwang In Kim1 , Florian Steinke2,3 , and Matthias Hein1
Department of Computer Science, Saarland University Saarbr?ucken, Germany
2
Siemens AG Corporate Technology Munich, Germany
3
MPI for Biological Cybern... | 3741 |@word polynomial:10 norm:7 stronger:1 nd:2 bf:2 seems:1 rgb:2 moment:1 reduction:7 contains:2 shum:1 recovered:1 com:1 discretization:1 dx:1 written:1 extrapolating:1 plot:1 selected:1 parameterization:2 parametrization:1 short:1 provides:1 revisited:1 preference:1 saarland:1 along:6 constructed:1 symposium:2 con... |
3,026 | 3,742 | Polynomial Semantic Indexing
Bing Bai(1)
Kunihiko Sadamasa(1)
Jason Weston(1)(2)
Yanjun Qi(1)
David Grangier(1)
Corinna Cortes(2)
Ronan Collobert(1)
Mehryar Mohri(2)(3)
(1)
NEC Labs America, Princeton, NJ
{bbai, dgrangier, collober, kunihiko, yanjun}@nec-labs.com
(2)
Google Research, New York, NY
{jweston, corinna,... | 3742 |@word kulis:1 briefly:1 version:3 polynomial:19 plsa:4 eng:1 pick:1 mention:1 versatile:1 bai:2 liu:2 contains:2 score:5 tuned:1 document:76 interestingly:1 outperforms:4 existing:2 current:1 com:5 yet:1 stemming:1 ronan:1 realistic:1 hofmann:1 designed:1 atlas:12 update:2 hash:8 intelligence:1 short:1 renshaw:1 ... |
3,027 | 3,743 | Clustering Sequence Sets for Motif Discovery
Jong Kyoung Kim and Seungjin Choi
Department of Computer Science
Pohang University of Science and Technology
San 31 Hyoja-dong, Nam-gu
Pohang 790-784, Korea
{blkimjk,seungjin}@postech.ac.kr
Abstract
Most of existing methods for DNA motif discovery consider only a single set... | 3743 |@word seek:1 kent:1 configuration:1 liu:5 selecting:1 bejerano:1 reynolds:1 outperforms:2 existing:3 current:1 comparing:1 ij1:2 written:1 hou:1 partition:5 plm:1 remove:1 treating:1 designed:1 siepel:1 half:1 discovering:5 generative:5 kyoung:1 short:1 core:1 detecting:2 location:1 five:5 phylogenetic:1 construc... |
3,028 | 3,744 | STDP enables spiking neurons to
detect hidden causes of their inputs
Bernhard Nessler, Michael Pfeiffer, and Wolfgang Maass
Institute for Theoretical Computer Science, Graz University of Technology
A-8010 Graz, Austria
{nessler,pfeiffer,maass}@igi.tugraz.at
Abstract
The principles by which spiking neurons contribute ... | 3744 |@word version:1 simulation:4 pick:1 thereby:1 solid:1 moment:2 reduction:2 efficacy:1 document:1 current:8 written:2 subsequent:1 realistic:2 plasticity:6 enables:1 update:4 fund:1 v:1 generative:7 discovering:4 ith:1 reciprocal:1 provides:6 contribute:1 node:1 simpler:2 mathematical:2 become:1 beta:1 profound:1 ... |
3,029 | 3,745 | An LP View of the M-best MAP problem
Menachem Fromer
Amir Globerson
School of Computer Science and Engineering
The Hebrew University of Jerusalem
{fromer,gamir}@cs.huji.ac.il
Abstract
We consider the problem of finding the M assignments with maximum
probability in a probabilistic graphical model. We show how this pro... | 3745 |@word version:1 polynomial:1 seems:1 nd:10 barahona:1 seek:1 configuration:4 interestingly:1 existing:1 steiner:2 current:1 intriguing:1 must:2 subsequent:1 partition:3 remove:2 update:1 intelligence:3 leaf:1 amir:1 plane:4 prize:2 characterization:7 certificate:6 node:7 provides:2 math:1 five:1 mathematical:1 di... |
3,030 | 3,746 | Discrete MDL Predicts in Total Variation
Marcus Hutter
RSISE @ ANU and SML @ NICTA
Canberra, ACT, 0200, Australia
marcus@hutter1.net www.hutter1.net
Abstract
The Minimum Description Length (MDL) principle selects the model that has the
shortest code for data plus model. We show that for a countable class of models,
MD... | 3746 |@word version:4 compression:1 stronger:2 nd:1 suitably:1 crucially:1 decomposition:2 q1:6 carry:2 reduction:1 celebrated:2 series:4 contains:3 prefix:2 past:2 existing:1 written:2 partition:3 stationary:8 generative:3 selected:6 intelligence:3 short:1 farther:1 num:1 simpler:1 mathematical:3 along:1 become:1 prov... |
3,031 | 3,747 | Help or Hinder: Bayesian Models of
Social Goal Inference
Tomer D. Ullman, Chris L. Baker, Owen Macindoe, Owain Evans,
Noah D. Goodman and Joshua B. Tenenbaum
{tomeru, clbaker, owenm, owain, ndg, jbt}@mit.edu
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Abstract
Everyday social inte... | 3747 |@word trial:4 illustrating:1 inversion:1 seems:1 proportion:2 open:1 accounting:1 recursively:1 initial:1 configuration:1 selecting:2 preverbal:1 animated:1 wako:1 existing:2 current:1 comparing:1 surprising:1 activation:1 yet:1 issuing:1 must:1 evans:1 additive:1 realistic:1 subsequent:1 entrance:1 shape:3 zacks... |
3,032 | 3,748 | Augmenting Feature-driven fMRI Analyses:
Semi-supervised Learning and Resting State Activity
Matthew B. Blaschko
Visual Geometry Group
Department of Engineering Science
University of Oxford
blaschko@robots.ox.ac.uk
Jacquelyn A. Shelton
Max Planck Institute for Biological Cybernetics
Fakult?at f?ur Informations- und K... | 3748 |@word trial:4 hampson:2 mri:1 sex:1 instruction:2 integrative:1 pearlson:2 tr:1 extrastriate:2 reduction:1 necessity:1 series:1 foveal:2 loc:3 subjective:1 current:1 activation:14 yet:1 attracted:1 must:1 kiebel:1 realistic:1 subsequent:1 visible:1 enables:1 motor:2 zacks:1 designed:1 haxby:1 implying:1 generativ... |
3,033 | 3,749 | Locality-Sensitive Binary Codes
from Shift-Invariant Kernels
Maxim Raginsky
Duke University
Durham, NC 27708
m.raginsky@duke.edu
Svetlana Lazebnik
UNC Chapel Hill
Chapel Hill, NC 27599
lazebnik@cs.unc.edu
Abstract
This paper addresses the problem of designing binary codes for high-dimensional
data such that vectors t... | 3749 |@word middle:2 proportion:1 stronger:2 norm:3 suitably:1 c0:2 unif:5 bn:2 moment:1 initial:1 series:3 document:1 ours:1 existing:1 scatter:6 dx:10 fn:3 concatenate:1 shape:1 plot:7 gist:2 progressively:1 hash:1 v:3 half:1 xk:6 ith:1 short:1 quantizer:1 mcdiarmid:1 c22:1 mathematical:1 c2:2 qualitative:1 prove:3 n... |
3,034 | 375 | Optimal Sampling of Natural Images: A Design
Principle for the Visual System?
William Bialek, a,b Daniel L. Ruderman, a and A. Zee C
a Department of Physics, and
Department of Molecular and Cell Biology
University of California at Berkeley
Berkeley, California 94720
bNEC Research Institute
4 Independence Way
Princeto... | 375 |@word seems:1 tr:1 carry:1 moment:1 foveal:1 daniel:1 imaginary:1 must:6 john:1 informative:1 webster:1 fund:2 stationary:1 isotropic:1 beginning:1 provides:1 mathematical:1 direct:1 ik:2 qualitative:1 cray:1 combine:1 expected:1 indeed:5 mechanic:2 multi:1 vertebrate:4 becomes:3 provided:1 begin:1 bounded:1 maxim... |
3,035 | 3,750 | Kernel Choice and Classifiability for RKHS
Embeddings of Probability Distributions
Bharath K. Sriperumbudur
Department of ECE
UC San Diego, La Jolla, USA
bharathsv@ucsd.edu
Kenji Fukumizu
The Institute of Statistical Mathematics
Tokyo, Japan
fukumizu@ism.ac.jp
Arthur Gretton
Carnegie Mellon University
MPI for Biolog... | 3750 |@word version:1 briefly:1 mmds:2 stronger:2 nd:1 heuristically:3 harder:1 rkhs:14 fa8750:1 outperforms:1 com:1 comparing:2 gmail:1 dx:1 must:2 written:2 universality:1 yet:1 w911nf0810242:1 plot:5 v:2 selected:3 accepting:1 characterization:6 provides:5 complication:1 zhang:1 along:1 c2:3 direct:2 become:1 introd... |
3,036 | 3,751 | Bayesian Source Localization with the
Multivariate Laplace Prior
Marcel van Gerven1,2 Botond Cseke1 Robert Oostenveld2 Tom Heskes1,2
1
Institute for Computing and Information Sciences
2
Donders Institute for Brain, Cognition and Behaviour
Radboud University Nijmegen
Nijmegen, The Netherlands
Abstract
We introduce a n... | 3751 |@word neurophysiology:1 trial:3 qthat:1 mri:2 inversion:1 norm:3 stronger:1 squid:1 covariance:4 decomposition:4 minus:1 moment:8 series:2 current:12 discretization:1 si:43 activation:2 written:3 must:3 john:1 numerical:7 distant:1 visible:1 realistic:2 drop:1 plot:2 update:7 ainen:1 cue:1 intelligence:1 tone:4 a... |
3,037 | 3,752 | Label Selection on Graphs
Andrew Guillory
Department of Computer Science
University of Washington
guillory@cs.washington.edu
Jeff Bilmes
Department of Electrical Engineering
University of Washington
bilmes@ee.washington.edu
Abstract
We investigate methods for selecting sets of labeled vertices for use in predicting
... | 3752 |@word trial:5 middle:2 version:1 polynomial:3 seems:4 open:2 termination:1 tried:5 carry:1 initial:1 liu:1 series:1 contains:2 selecting:2 current:1 surprising:1 yet:1 must:3 partition:3 mirzazadeh:1 remove:1 aside:1 greedy:4 selected:6 pelckmans:6 ith:1 provides:2 node:23 attack:1 simpler:1 constructed:2 incorre... |
3,038 | 3,753 | On Learning Rotations
Raman Arora
University of Wisconsin-Madison
Department of Electrical and Computer Engineering
1415 Engineering Drive, Madison, WI 53706
rmnarora@u.washington.edu
Abstract
An algorithm is presented for online learning of rotations. The proposed algorithm
involves matrix exponentiated gradient upd... | 3753 |@word determinant:3 version:9 repository:1 norm:9 open:1 calculus:1 closure:1 seek:1 simulation:3 pick:1 frigyik:1 tr:9 reduction:4 series:1 current:1 comparing:1 com:1 written:2 john:1 evans:1 additive:1 shape:2 plot:3 update:46 v:1 rrt:5 intelligence:2 warmuth:5 steepest:3 smith:2 manfred:3 provides:1 draft:1 b... |
3,039 | 3,754 | A Rate Distortion Approach for Semi-Supervised
Conditional Random Fields
Yang Wang??
Gholamreza Haffari??
?
School of Computing Science
Simon Fraser University
Burnaby, BC V5A 1S6, Canada
{ywang12,ghaffar1,mori}@cs.sfu.ca
Shaojun Wang?
Greg Mori?
?
Kno.e.sis Center
Wright State University
Dayton, OH 45435, USA
shaojun... | 3754 |@word trial:2 achievable:1 compression:7 nd:1 tried:1 tkacik:1 tr:1 solid:1 electronics:1 configuration:2 contains:3 liu:1 initial:1 bc:1 ours:2 outperforms:5 comparing:1 si:1 written:7 informative:1 kdd:1 discrimination:1 alone:1 generative:6 selected:2 v:5 mccallum:3 sys:1 provides:1 boosting:2 parameterization... |
3,040 | 3,755 | Nash Equilibria of Static Prediction Games
?
Michael Bruckner
Department of Computer Science
University of Potsdam, Germany
mibrueck@cs.uni-potsdam.de
Tobias Scheffer
Department of Computer Science
University of Potsdam, Germany
scheffer@cs.uni-potsdam.de
Abstract
The standard assumption of identically distributed t... | 3755 |@word private:6 norm:2 seek:2 covariance:1 natsoulis:1 attainable:1 pressure:1 incurs:1 thereby:2 shot:3 initial:2 contains:2 selecting:1 bhattacharyya:1 outperforms:2 existing:1 past:1 john:1 additive:1 christian:1 v:5 aside:1 amir:3 oldest:1 alterable:1 firstly:1 consists:1 bertrand:1 decreasing:1 td:1 increasi... |
3,041 | 3,756 | An Integer Projected Fixed Point Method for Graph
Matching and MAP Inference
Marius Leordeanu
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
leordeanu@gmail.com
Martial Hebert
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
hebert@ri.cmu.edu
Rahul Sukthankar
Intel Labs Pittsbur... | 3756 |@word version:4 norm:1 accommodate:1 initial:7 score:49 ours:2 outperforms:6 existing:1 current:3 com:1 discretization:5 comparing:1 gmail:1 must:2 shape:3 wanted:1 drop:2 update:1 v:1 alone:3 intelligence:4 fewer:1 selected:1 xk:28 node:3 firstly:1 five:1 dell:1 along:5 consists:2 inside:1 introduce:3 x0:4 pairw... |
3,042 | 3,757 | Estimating image bases for visual image
reconstruction from human brain activity
Yusuke Fujiwara1 Yoichi Miyawaki2,1 Yukiyasu Kamitani1
1
ATR Computational Neuroscience Laboratories
2
National Institute of Information and Communications Technology
2-2-2 Hikaridai, Seika-cho, Kyoto, Japan
yureisoul@gmail.com yoichi m@a... | 3757 |@word trial:6 mri:1 nd:3 a02:1 covariance:2 p0:7 tr:1 configuration:1 foveal:2 etric:2 current:1 com:1 gmail:1 b01:1 informative:1 shape:18 intelligence:1 provides:2 location:1 successive:1 five:4 glover:1 constructed:4 consists:2 interscience:1 manner:1 expected:1 seika:1 multi:5 brain:5 spherical:1 automaticall... |
3,043 | 3,758 | Learning to Rank by Optimizing NDCG Measure
Hamed Valizadegan
Rong Jin
Computer Science and Engineering
Michigan State University
East Lansing, MI 48824
{valizade,rongjin}@cse.msu.edu
Ruofei Zhang
Jianchang Mao
Advertising Sciences, Yahoo! Labs
4401 Great America Parkway,
Santa Clara, CA 95054
{rzhang,jmao}@yahoo-inc... | 3758 |@word exploitation:1 version:2 judgement:1 mcrank:3 nd:1 relevancy:5 liu:6 score:4 genetic:3 document:53 outperforms:1 past:1 existing:1 current:4 com:1 clara:1 attracted:1 written:3 john:1 r01gm079688:1 numerical:2 partition:2 listmle:1 designed:1 ainen:1 update:2 intelligence:2 advancement:1 ith:1 renshaw:1 pro... |
3,044 | 3,759 | Who?s Doing What: Joint Modeling of Names and
Verbs for Simultaneous Face and Pose Annotation
Luo Jie
Idiap and EPF Lausanne
jluo@idiap.ch
Barbara Caputo
Idiap Research Institute
bcaputo@idiap.ch
Vittorio Ferrari
ETH Zurich
ferrari@vision.ee.ethz.ch
Abstract
Given a corpus of news items consisting of images accompan... | 3759 |@word kulis:1 illustrating:1 middle:1 version:2 everingham:1 open:2 hu:13 carolina:1 concise:1 reduction:1 initial:2 contains:2 ecole:1 ours:1 document:13 outperforms:2 existing:2 freitas:2 recovered:1 com:3 luo:1 written:3 must:1 parsing:1 stemming:1 visible:2 happen:2 realistic:1 kdd:1 drop:1 update:2 progressi... |
3,045 | 376 | A Delay-Line Based
Motion Detection Chip
Tim Horiuchi t
John Lazzaro?
Andrew Moore t
Christof Koch t
tComputation and Neural Systems Program
?Department of Computer Science
California Institute of Technology MS 216-76
Pasadena, CA 91125
Abstract
Inspired by a visual motion detection model for the ra.bbit retina
a... | 376 |@word aircraft:1 version:2 rising:4 pulse:10 propagate:2 contains:2 tuned:1 interestingly:1 current:1 john:2 physiol:1 designed:2 plot:2 v:5 half:2 ial:1 short:1 filtered:2 sudden:1 provides:1 coarse:1 location:1 along:1 incorrect:2 inside:1 expected:3 ra:1 rapid:1 themselves:1 oscilloscope:2 aliasing:2 inspired:2... |
3,046 | 3,760 | Differential Use of Implicit Negative Evidence in
Generative and Discriminative Language Learning
Anne S. Hsu
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
{showen,tom griffiths}@berkeley.edu
Abstract
A classic debate in cognitive science revolves around understand... | 3760 |@word trial:1 stronger:1 proportion:3 nd:2 willing:1 seek:4 simulation:3 yaleu:1 moment:1 series:2 animated:2 current:1 anne:1 yet:1 written:4 designed:2 plot:1 v:1 alone:1 generative:44 credence:1 selected:1 leaf:1 beginning:1 ith:1 filtered:1 provides:1 contribute:1 location:1 mathematical:1 unacceptable:1 cons... |
3,047 | 3,761 | Robust Value Function Approximation Using
Bilinear Programming
Shlomo Zilberstein
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
shlomo@cs.umass.edu
Marek Petrik
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
petrik@cs.umass.edu
Abstract
Existing value func... | 3761 |@word version:1 briefly:1 polynomial:3 norm:15 risto:1 heuristically:1 pieter:1 reduction:4 initial:1 series:2 uma:2 selecting:1 daniel:1 interestingly:1 existing:5 current:1 must:1 john:1 ronald:3 realistic:1 shlomo:7 designed:2 update:1 progressively:1 stationary:1 greedy:10 selected:1 intelligence:5 short:1 pr... |
3,048 | 3,762 | Submodularity Cuts and Applications
Yoshinobu Kawahara?
The Inst. of Scientific and Industrial Res. (ISIR),
Osaka Univ., Japan
Kiyohito Nagano
Dept. of Math. and Comp. Sci.,
Tokyo Inst. of Technology, Japan
kawahara@ar.sanken.osaka-u.ac.jp
nagano@is.titech.ac.jp
Koji Tsuda
Comp. Bio. Research Center,
AIST, Japan
J... | 3762 |@word briefly:1 eliminating:1 polynomial:4 suitably:1 willing:1 d2:1 seek:1 covariance:1 p0:4 isir:1 reduction:2 initial:1 contains:2 selecting:1 document:3 existing:7 current:10 com:1 si:8 predetermined:2 enables:2 bilp:6 treating:1 update:5 v:1 greedy:11 selected:6 half:1 item:1 plane:16 beginning:1 provides:1 ... |
3,049 | 3,763 | Matrix Completion from Power-Law Distributed
Samples
Raghu Meka, Prateek Jain, and Inderjit S. Dhillon
Department of Computer Sciences
University of Texas at Austin
Austin, TX 78712
{raghu,pjain,inderjit}@cs.utexas.edu
Abstract
The low-rank matrix completion problem is a fundamental problem with many
important applic... | 3763 |@word middle:1 version:1 stronger:2 norm:3 suitably:1 termination:1 km:2 simulation:1 crucially:2 q1:4 contains:3 lightweight:1 rightmost:1 outperforms:2 existing:3 comparing:1 reminiscent:1 realistic:4 partition:2 kdd:2 hypothesize:2 plot:4 drop:1 progressively:2 v:1 yr:5 prize:1 qjk:2 authority:1 node:3 simpler... |
3,050 | 3,764 | fMRI-Based Inter-Subject Cortical Alignment Using
Functional Connectivity
Bryan R. Conroy1 Benjamin D. Singer2 James V. Haxby3? Peter J. Ramadge1
1
Department of Electrical Engineering, 2 Neuroscience Institute, Princeton University
3
Department of Psychology, Dartmouth College
Abstract
The inter-subject alignment of... | 3764 |@word trial:1 mri:3 manageable:1 norm:3 suitably:1 d2:1 seek:3 azimuthal:1 vek:5 simplifying:1 contraction:1 decomposition:3 bachman:1 incurs:1 fif:1 tr:5 initial:5 series:19 existing:1 current:3 ka:1 nt:2 comparing:1 must:5 readily:1 mesh:15 subsequent:1 fn:1 hajnal:1 shape:1 enables:1 haxby:1 atlas:1 update:4 m... |
3,051 | 3,765 | A unified framework for high-dimensional analysis of
M -estimators with decomposable regularizers
Sahand Negahban
Department of EECS
UC Berkeley
sahand n@eecs.berkeley.edu
Pradeep Ravikumar
Department of Computer Sciences
UT Austin
pradeepr@cs.utexas.edu
Martin J. Wainwright
Department of Statistics
Department of EE... | 3765 |@word determinant:1 version:9 achievable:1 norm:31 turlach:1 suitably:1 d2:2 km:3 bn:1 covariance:3 decomposition:2 series:2 exclusively:1 past:2 wainwrig:1 existing:2 must:5 subsequent:2 accordingly:1 ith:1 probablity:1 provides:2 allerton:1 zhang:1 c2:6 yuan:1 prove:1 shorthand:1 consists:2 combine:1 manner:2 i... |
3,052 | 3,766 | Region-based Segmentation and Object Detection
Stephen Gould1
Tianshi Gao1
Daphne Koller2
Department of Electrical Engineering, Stanford University
2
Department of Computer Science, Stanford University
{sgould,tianshig,koller}@cs.stanford.edu
1
Abstract
Object detection and multi-class image segmentation are two clo... | 3766 |@word rreg:3 version:3 briefly:2 dalal:3 middle:2 eliminating:1 logit:2 triggs:3 r:3 decomposition:5 covariance:1 textonboost:1 thereby:1 reduction:1 initial:2 liu:2 configuration:2 score:4 hoiem:3 fevrier:1 ours:1 current:5 contextual:7 assigning:1 reminiscent:1 parsing:3 visible:1 wiewiora:1 informative:1 shape... |
3,053 | 3,767 | A Generalized Natural Actor-Critic Algorithm
Tetsuro Morimura? , Eiji Uchibe? , Junichiro Yoshimoto? , Kenji Doya?
?: IBM Research ? Tokyo, Kanagawa, Japan
?: Okinawa Institute of Science and Technology, Okinawa, Japan
tetsuro@jp.ibm.com, {uchibe,jun-y,doya}@oist.jp
Abstract
Policy gradient Reinforcement Learning (RL)... | 3767 |@word determinant:1 version:1 briefly:1 instrumental:12 seems:1 covariance:1 pg:2 q1:2 initial:2 substitution:1 efficacy:1 interestingly:1 existing:1 current:5 com:1 si:1 written:1 must:1 numerical:6 update:2 stationary:10 intelligence:2 selected:1 accordingly:5 steepest:3 ith:1 provides:1 sigmoidal:1 zhang:1 una... |
3,054 | 3,768 | Relax then Compensate:
On Max-Product Belief Propagation and More
Adnan Darwiche
Computer Science Department
University of California, Los Angeles
Los Angeles, CA 90095
darwiche@cs.ucla.edu
Arthur Choi
Computer Science Department
University of California, Los Angeles
Los Angeles, CA 90095
aychoi@cs.ucla.edu
Abstract... | 3768 |@word mild:1 adnan:5 open:1 confirms:1 seek:4 simplifying:1 dramatic:1 initial:5 configuration:9 contains:2 existing:1 rish:1 tackling:1 must:2 dechter:4 partition:1 remove:3 update:4 v:1 alone:5 intelligence:1 fewer:1 amir:2 provides:2 coarse:2 node:2 allerton:1 simpler:3 daphne:1 become:1 darwiche:6 manner:1 in... |
3,055 | 3,769 | Fast, smooth and adaptive regression in metric spaces
Samory Kpotufe
UCSD CSE
Abstract
It was recently shown that certain nonparametric regressors can escape the curse
of dimensionality when the intrinsic dimension of data is low ([1, 2]). We prove
some stronger results in more general settings. In particular, we con... | 3769 |@word middle:1 polynomial:1 achievable:1 stronger:1 norm:1 c0:3 grey:2 decomposition:1 pick:2 reduction:3 initial:1 selecting:1 past:2 current:2 beygelzimer:1 yet:2 written:1 parsing:1 must:2 fn:29 partition:6 half:1 selected:4 nq:30 beginning:2 farther:1 cse:1 node:6 mcdiarmid:1 n1q:1 h4:5 c2:3 descendant:3 prov... |
3,056 | 377 | Proximity Effect Corrections in Electron Beam
Lithography Using a Neural Network
Robert C. Frye
AT &T Bell Laboratories
600 Mountain Avenue
Murray Hill. NJ 08854
Kevin D. Cummings*
AT&T Bell Laboratories
600 Mountain Avenue
Murray Hill. NJ 08854
Edward A. Rietman
AT&T Bell Laboratories
600 Mountain Avenue
Murray Hil... | 377 |@word trial:4 inversion:1 instruction:1 simulation:1 initial:2 configuration:1 comparing:1 written:6 readily:2 must:2 shape:1 prohibitive:1 device:1 fewer:1 leamed:1 smith:1 record:1 ire:1 node:3 contribute:1 mathematical:2 direct:1 consists:1 combine:1 ray:1 mask:1 expected:1 decomposed:1 resolve:1 little:1 motor... |
3,057 | 3,770 | Heavy-Tailed Symmetric Stochastic Neighbor
Embedding
Zhirong Yang
The Chinese University of Hong Kong
Helsinki University of Technology
zhirong.yang@tkk.fi
Irwin King
The Chinese University of Hong Kong
king@cse.cuhk.edu.hk
Zenglin Xu
The Chinese University of Hong Kong
Saarland University & MPI for Informatics
zlxu... | 3770 |@word kong:4 repository:1 middle:1 briefly:1 compression:2 instruction:1 seek:1 tried:1 versatile:1 accommodate:2 crowding:2 reduction:7 score:6 tuned:1 current:2 comparing:1 attracted:1 subsequent:1 informative:1 plot:4 update:9 stationary:1 intelligence:3 selected:1 cook:1 provides:1 characterization:1 cse:2 si... |
3,058 | 3,771 | Human Rademacher Complexity
Xiaojin Zhu1 , Timothy T. Rogers2 , Bryan R. Gibson1
Department of {1 Computer Sciences, 2 Psychology}
University of Wisconsin-Madison. Madison, WI 15213
jerryzhu@cs.wisc.edu, ttrogers@wisc.edu, bgibson@cs.wisc.edu
Abstract
We propose to use Rademacher complexity, originally developed in c... | 3771 |@word proceeded:1 middle:1 seems:2 hippocampus:1 instruction:1 minus:1 solid:1 harder:2 contains:2 chervonenkis:2 existing:2 comparing:2 universality:1 yet:1 zhu1:1 conjunctive:1 happen:1 shape:28 rote:1 christian:1 joy:1 v:3 half:1 instantiate:1 guess:1 item:10 intelligence:1 accordingly:1 footing:1 short:3 fa95... |
3,059 | 3,772 | Reconstruction of Sparse Circuits Using
Multi-neuronal Excitation (RESCUME)
Tao Hu and Dmitri B. Chklovskii
Janelia Farm Research Campus, HHMI
19700 Helix Drive, Ashburn, VA 20147
hut, mitya@janelia.hhmi.org
Abstract
One of the central problems in neuroscience is reconstructing synaptic
connectivity in neural circuit... | 3772 |@word neurophysiology:4 trial:41 milenkovic:1 norm:3 hu:1 simulation:7 pulse:1 simplifying:2 postsynaptically:1 solid:1 briggman:1 carry:2 deisseroth:1 contains:3 efficacy:1 united:4 tuned:1 genetic:1 existing:2 current:4 recovered:2 nt:1 luo:1 clements:1 activation:1 yet:2 si:2 must:2 ust:1 cottrell:1 realistic:... |
3,060 | 3,773 | Modeling Social Annotation Data
with Content Relevance using a Topic Model
Tomoharu Iwata
Takeshi Yamada
Naonori Ueda
NTT Communication Science Laboratories
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
{iwata,yamada,ueda}@cslab.kecl.ntt.co.jp
Abstract
We propose a probabilistic topic model for analyzing and ext... | 3773 |@word version:1 proportion:4 nd:9 git:1 pg:1 electronics:1 manmatha:3 contains:1 series:1 document:51 freitas:1 current:2 wd:4 com:4 lang:1 gmail:2 citeulike:2 kdd:1 hofmann:2 cheap:1 designed:1 concert:1 generative:7 fewer:4 cook:2 half:3 ubuntu:2 intelligence:1 mccallum:1 yamada:3 filtered:1 blei:4 mental:1 mat... |
3,061 | 3,774 | No evidence for active sparsification
in the visual cortex
Pietro Berkes, Benjamin L. White, and J?ozsef Fiser
Volen Center for Complex Systems
Brandeis University, Waltham, MA 02454
Abstract
The proposal that cortical activity in the visual cortex is optimized for sparse neural activity is one of the most established... | 3774 |@word deformed:1 trial:2 neurophysiology:3 eliminating:1 seems:2 norm:3 stronger:1 tr:11 reduction:3 current:2 comparing:2 nt:2 contextual:1 activation:4 dx:1 must:1 evans:1 additive:2 plasticity:1 shape:2 stationary:1 generative:10 selected:1 leaf:1 xk:19 beginning:1 postnatal:1 colored:2 tolhurst:2 gx:2 success... |
3,062 | 3,775 | Riffled Independence for Ranked Data
Jonathan Huang, Carlos Guestrin
School of Computer Science,
Carnegie Mellon University
{jch1,guestrin}@cs.cmu.edu
Abstract
Representing distributions over permutations can be a daunting task due to
the fact that the number of permutations of n objects scales factorially in n.
One ... | 3775 |@word groupwise:1 trial:1 version:1 briefly:1 interleave:1 seems:1 kondor:3 nd:1 unif:3 open:2 decomposition:1 invoking:1 pick:1 recursively:1 mcauley:1 initial:1 selecting:1 offering:1 written:3 must:2 realize:1 kdd:1 remove:2 drop:5 plot:6 selected:1 fewer:1 item:2 nq:1 warmuth:1 ith:2 draft:1 parameterizations... |
3,063 | 3,776 | Object discovery and identi?cation
Charles Kemp & Alan Jern
Department of Psychology
Carnegie Mellon University
{ckemp,ajern}@cmu.edu
Fei Xu
Department of Psychology
University of California, Berkeley
fei xu@berkeley.edu
Abstract
Humans are typically able to infer how many objects their environment contains
and to re... | 3776 |@word proportion:4 replicate:1 open:11 grey:1 essay:1 arti:1 shot:1 contains:5 series:1 rightmost:1 existing:1 yet:4 must:3 parsing:1 written:1 realize:1 planet:1 visible:2 subsequent:1 alphanumeric:1 partition:3 shape:4 informative:1 cant:1 realistic:1 plot:3 numerical:1 infant:3 generative:2 half:2 guess:1 item... |
3,064 | 3,777 | Matrix Completion from Noisy Entries
Raghunandan H. Keshavan?, Andrea Montanari??, and Sewoong Oh?
Abstract
Given a matrix M of low-rank, we consider the problem of reconstructing it from
noisy observations of a small, random subset of its entries. The problem arises
in a variety of applications, from collaborative f... | 3777 |@word version:3 norm:9 stronger:1 c0:8 crucially:1 decomposition:4 tr:5 initial:2 contains:3 zij:5 qth:1 toh:1 must:2 additive:1 numerical:9 guess:1 xk:8 zmax:3 vanishing:1 realizing:1 smith:1 caveat:1 provides:2 math:1 c2:8 vjk:1 edelman:1 prove:2 consists:1 introduce:1 x0:11 indeed:3 roughly:2 cand:5 behavior:1... |
3,065 | 3,778 | Probabilistic Relational PCA
Wu-Jun Li
Dit-Yan Yeung
Dept. of Comp. Sci. and Eng.
Hong Kong University of Science and Technology
Hong Kong, China
{liwujun,dyyeung}@cse.ust.hk
Zhihua Zhang
School of Comp. Sci. and Tech.
Zhejiang University
Zhejiang 310027, China
zhzhang@cs.zju.edu.cn
Abstract
One crucial assumption ma... | 3778 |@word kong:3 determinant:1 version:2 briefly:3 eliminating:1 loading:1 stronger:1 nd:6 d2:2 confirms:1 eng:1 covariance:11 tr:12 reduction:2 contains:2 series:2 document:3 outperforms:1 existing:2 comparing:1 chu:2 ust:2 wx:4 informative:1 kdd:3 hofmann:2 fund:1 generative:2 discovering:1 half:1 nq:3 mccallum:2 i... |
3,066 | 3,779 | Graph Zeta Function in the Bethe Free Energy and
Loopy Belief Propagation
Yusuke Watanabe
The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa
Tokyo 190-8562, Japan
watay@ism.ac.jp
Kenji Fukumizu
The Institute of Statistical Mathematics
10-3 Midori-cho, Tachikawa
Tokyo 190-8562, Japan
fukumizu@ism.ac.j... | 3779 |@word determinant:6 version:2 stronger:1 contraction:1 kappen:2 initial:1 cyclic:1 terminus:1 ue1:1 existing:1 si:4 must:4 written:1 ikeda:1 partition:1 koetter:3 pseudomarginals:5 update:7 midori:2 n0:1 stationary:3 spec:10 parametrization:1 reciprocal:2 vanishing:1 provides:1 math:2 characterization:1 mathemati... |
3,067 | 378 | A Neural Network Approach for
Three-Dimensional Object Recognition
Volker 'bap
Siemens AG, Central Reeearch and Development
Otto-HaIm-Ring 6, 0.8000 Munchen 83
GermaD)'
Ab.tract
The model-bued neural vision Iystem presented here determines the p~
aition and identity of three-dimensional objects. Two ltereo imagee of
... | 378 |@word linearized:2 initial:1 activation:2 must:1 visible:1 christian:1 designed:1 drop:1 simpler:1 zii:1 constructed:1 edelman:2 combine:1 dan:2 pf:5 becomes:4 matched:7 cm:1 interpreted:2 minimizes:1 developed:1 ag:1 transformation:2 certainty:1 pseudo:1 every:2 exactly:1 wrong:1 unit:1 grant:1 positive:1 local:3... |
3,068 | 3,780 | The Infinite Partially Observable Markov Decision
Process
Finale Doshi-Velez
Cambridge University
Cambridge, CB21PZ, UK
finale@alum.mit.edu
Abstract
The Partially Observable Markov Decision Process (POMDP) framework has
proven useful in planning domains where agents must balance actions that provide knowledge and acti... | 3780 |@word trial:2 middle:2 open:4 hu:1 homomorphism:1 recursively:1 initial:2 contains:1 series:2 past:2 current:10 must:8 periodically:1 analytic:2 qmdp:1 designed:1 plot:1 update:4 v:1 alone:1 generative:2 leaf:2 fewer:1 resampling:1 prohibitive:1 stationary:1 intelligence:3 beginning:2 ith:1 smith:1 prespecified:2... |
3,069 | 3,781 | Adapting to the Shifting Intent of Search Queries?
Umar Syed?
Department of Computer
and Information Science
University of Pennsylvania
Philadelphia, PA 19104
usyed@cis.upenn.edu
Aleksandrs Slivkins
Microsoft Research
Mountain View, CA 94043
slivkins@microsoft.com
Nina Mishra
Microsoft Research
Mountain View, CA 9404... | 3781 |@word mild:2 version:7 achievable:1 stronger:2 seems:1 nd:3 seek:1 incurs:2 dramatic:1 carry:1 moment:1 liu:1 series:2 score:2 exclusively:1 contains:2 tuned:1 document:5 ours:1 outperforms:2 mishra:2 existing:2 current:1 com:2 nt:5 contextual:1 must:4 john:1 subsequent:1 happen:1 numerical:1 kdd:2 designed:1 dro... |
3,070 | 3,782 | Neural Implementation of Hierarchical Bayesian
Inference by Importance Sampling
Thomas L. Griffiths
Department of Psychology
University of California, Berkeley
Berkeley, CA 94720
tom griffiths@berkeley.edu
Lei Shi
Helen Wills Neuroscience Institute
University of California, Berkeley
Berkeley, CA 94720
lshi@berkeley.e... | 3782 |@word trial:4 proportion:1 seems:2 stronger:1 simulation:7 propagate:1 decomposition:1 recursively:1 reduction:1 valois:1 tuned:5 ording:1 reynolds:1 current:1 recovered:1 nt:2 activation:3 assigning:2 dx:4 plasticity:1 shape:1 enables:1 motor:1 discrimination:4 xxz:1 cue:15 generative:14 guess:1 nervous:4 device... |
3,071 | 3,783 | Linearly constrained Bayesian matrix factorization
for blind source separation
Mikkel N. Schmidt
Department of Engineering
University of Cambridge
mns@imm.dtu.dk
Abstract
We present a general Bayesian approach to probabilistic matrix factorization subject to linear constraints. The approach is based on a Gaussian obs... | 3783 |@word proportion:1 grey:1 simulation:1 decomposition:3 covariance:8 decorrelate:1 q1:1 brightness:1 contains:1 existing:2 current:2 written:1 must:1 additive:3 interpretable:4 update:1 stationary:1 intelligence:1 selected:1 nq:4 isotropic:2 inversegamma:1 ith:3 short:1 colored:1 node:1 contribute:1 constructed:1 ... |
3,072 | 3,784 | Orthogonal Matching Pursuit from
Noisy Measurements: A New Analysis?
Sundeep Rangan
Qualcomm Technologies
Bedminster, NJ
srangan@qualcomm.com
Alyson K. Fletcher
University of California, Berkeley
Berkeley, CA
alyson@eecs.berkeley.edu
Abstract
A well-known analysis of Tropp and Gilbert shows that orthogonal matching
... | 3784 |@word milenkovic:2 version:1 itrue:32 nd:1 open:1 calculus:1 accounting:1 decomposition:3 eng:2 harder:1 selecting:1 ecole:1 com:1 comparing:1 luo:1 must:1 numerical:2 treating:1 tarokh:2 greedy:1 selected:2 fewer:1 sys:2 provides:5 math:4 location:2 toronto:1 simpler:1 zhang:1 along:2 incorrect:5 symp:1 indeed:1... |
3,073 | 3,785 | A Biologically Plausible Model for Rapid Natural
Image Identification
S. Ghebreab, A. W.M. Smeulders
Intelligent Sensory Information Systems Group
University of Amsterdam, The Netherlands
s.ghebreab@uva.nl
H. S. Schoite, V.A.F. Lamme
Cognitive Neuroscience Group
University of Amsterdam, The Netherlands
h.s.scholte@uv... | 3785 |@word trial:3 biosemi:2 middle:1 worsens:1 suitably:1 disk:7 hu:1 decomposition:1 arti:1 minus:1 carry:2 configuration:1 contains:2 score:2 selecting:2 tuned:2 i3n:2 realistic:3 j1:2 blur:1 shape:6 plot:3 gist:8 drop:1 aside:1 half:1 selected:6 leaf:3 intelligence:2 indicative:1 filtered:1 provides:4 contribute:1... |
3,074 | 3,786 | Generalization Errors and Learning Curves for
Regression with Multi-task Gaussian Processes
Kian Ming A. Chai
School of Informatics, University of Edinburgh,
10 Crichton Street, Edinburgh EH8 9AB, UK
k.m.a.chai@ed.ac.uk
Abstract
We provide some insights into how task correlations in multi-task Gaussian process (GP) r... | 3786 |@word middle:2 version:1 proportion:4 seek:2 simulation:3 covariance:13 tr:2 solid:3 harder:2 uncovered:1 series:1 existing:1 current:1 comparing:3 nt:3 recovered:1 michal:1 yet:1 dx:11 chu:1 readily:1 visible:1 numerical:2 hofmann:1 plot:3 n0:2 stationary:1 intelligence:4 assurance:1 indicative:1 isotropic:7 ith... |
3,075 | 3,787 | DUOL: A Double Updating Approach for
Online Learning
Peilin Zhao
Steven C.H. Hoi
Rong Jin
School of Comp. Eng.
Nanyang Tech. University
Singapore 639798
School of Comp. Eng.
Nanyang Tech. University
Singapore 639798
Dept. of Comp. Sci. & Eng.
Michigan State University
East Lansing, MI, 48824
zhao0106@ntu.edu.sg
... | 3787 |@word trial:10 repository:2 version:2 seems:1 dekel:5 eng:3 score:6 seriously:1 spambase:4 existing:13 current:8 comparing:2 assigning:3 kft:4 designed:3 update:16 selected:2 website:1 plane:1 short:2 cse:1 introduce:1 lansing:1 examine:2 multi:1 brain:1 decreasing:2 encouraging:1 cpu:1 becomes:1 fti:5 bounded:8 ... |
3,076 | 3,788 | Parallel Inference for Latent Dirichlet Allocation on
Graphics Processing Units
Ningyi Xu
Microsoft Research Asia
No. 49 Zhichun Road
Beijing, P.R. China
Feng Yan
Department of CS
Purdue University
West Lafayette, IN 47907
Yuan (Alan) Qi
Departments of CS and Statistics
Purdue University
West Lafayette, IN 47907
Ab... | 3788 |@word mild:1 briefly:1 xtest:5 pick:1 curtail:1 tr:5 reduction:1 initial:2 zij:13 njk:9 document:26 ours:2 o2:1 outperforms:1 current:4 gpu:20 numerical:1 partition:24 j1:4 enables:3 update:5 intelligence:1 prohibitive:1 device:13 ith:1 core:2 provides:1 zhang:2 yuan:1 consists:3 combine:1 sync:6 overhead:2 insid... |
3,077 | 3,789 | Bilinear classifiers for visual recognition
Hamed Pirsiavash
Deva Ramanan
Charless Fowlkes
Department of Computer Science
University of California at Irvine
{hpirsiav,dramanan,fowlkes}@ics.uci.edu
Abstract
We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a
discriminative variant of biline... | 3789 |@word multitask:1 middle:1 dalal:5 advantageous:1 norm:3 seems:1 triggs:3 everingham:1 decomposition:1 citeseer:2 tr:20 reduction:5 initial:1 score:6 tuned:1 suppressing:1 outperforms:2 existing:3 current:1 nt:1 si:3 assigning:1 written:1 wx:17 dive:3 hofmann:1 update:1 v:1 intelligence:1 fewer:2 plane:2 detectin... |
3,078 | 379 | FEEDBACK SYNAPSE TO CONE AND LIGHT ADAPTATION
Josef Skrzypek
Machine Perception Laboratory
UCLA - Los Angeles, California 90024
INTERNET: SKRZYPEK@CS.UCLA.EDU
Abstract
Light adaptation (LA) allows cone vIslOn to remain functional between
twilight and the brightest time of day even though, at anyone time, their
intens... | 379 |@word middle:3 compression:3 seems:1 proportion:1 hyperpolarized:2 open:4 simulation:2 mohm:2 cyclic:1 series:4 current:12 comparing:1 od:1 must:1 physiol:5 hyperpolarizing:5 shape:2 plot:2 alone:2 half:1 gfb:6 contribute:1 lor:1 along:9 consists:1 sustained:3 manner:1 behavior:1 abscissa:4 ol:1 decreasing:2 littl... |
3,079 | 3,790 | Measuring Invariances in Deep Networks
Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
{ia3n,quocle,asaxe,hllee,ang}@cs.stanford.edu
Abstract
For many pattern recognition tasks, the ideal input feature would be invariant to
mu... | 3790 |@word version:1 proportion:4 seek:1 tried:1 lobe:1 initial:1 contains:1 score:30 document:1 current:1 surprising:1 activation:7 yet:2 si:8 must:1 enables:1 designed:3 plot:3 progressively:2 medial:1 v:1 credence:1 greedy:2 generative:1 une:1 plane:12 fried:1 provides:3 location:1 successive:1 height:1 become:4 co... |
3,080 | 3,791 | Learning transport operators for image manifolds
Bruno A. Olshausen
Helen Wills Neuroscience Institute
& School of Optometry
University of California, Berkeley
Berkeley, CA 94720
baolshausen@berkeley.edu
Benjamin J. Culpepper
Department of EECS
Computer Science Division
University of California, Berkeley
Berkeley, CA... | 3791 |@word economically:1 unaltered:1 middle:1 compression:1 norm:7 seek:1 decomposition:5 pick:1 tr:1 reduction:2 initial:2 series:2 africa:1 recovered:2 must:2 optometry:1 subsequent:1 numerical:2 periodically:1 blur:1 enables:1 plot:1 drop:1 update:1 progressively:1 generative:2 selected:4 amir:1 short:1 mitigation... |
3,081 | 3,792 | Localizing Bugs in Program Executions
with Graphical Models
Valentin Dallmeier
Saarland University
Saarbruecken, Germany
dallmeier@cs.uni-saarland.de
Laura Dietz
Max-Planck Institute for Computer Science
Saarbruecken, Germany
dietz@mpi-inf.mpg.de
Andreas Zeller
Saarland University
Saarbruecken, Germany
zeller@cs.uni-... | 3792 |@word repository:2 faculty:1 kapil:1 bigram:1 proportion:1 open:1 confirms:1 solid:1 initial:1 contains:5 fragment:30 score:4 envision:1 outperforms:2 current:1 anne:1 si:5 yet:3 john:1 realistic:1 designed:3 treating:1 plot:1 generative:9 selected:2 mccallum:1 record:1 blei:1 infrastructure:1 provides:1 location... |
3,082 | 3,793 | Efficient Learning using Forward-Backward Splitting
John Duchi
University of California Berkeley
jduchi@cs.berkeley.edu
Yoram Singer
Google
singer@google.com
Abstract
We describe, analyze, and experiment with a new framework for empirical loss
minimization with regularization. Our algorithmic framework alternates be... | 3793 |@word kgk:1 middle:3 version:5 bigram:1 norm:33 proportion:1 justice:1 hu:1 calculus:1 d2:2 seek:1 simulation:1 minus:2 boundedness:2 initial:1 contains:2 document:1 rightmost:2 outperforms:1 existing:2 com:1 skipping:1 yet:2 must:2 readily:2 john:1 enables:2 hypothesize:1 plot:5 designed:2 update:15 stationary:1... |
3,083 | 3,794 | Statistical Models of Linear and Non?linear
Contextual Interactions in Early Visual Processing
Ruben Coen?Cagli
AECOM
Bronx, NY 10461
rcagli@aecom.yu.edu
Peter Dayan
GCNU, UCL
17 Queen Square, LONDON
dayan@gatsby.ucl.ac.uk
Odelia Schwartz
AECOM
Bronx, NY 10461
oschwart@aecom.yu.edu
Abstract
A central hypothesis abo... | 3794 |@word neurophysiology:2 version:2 middle:1 compression:1 stronger:2 seems:1 wenderoth:1 open:2 hyv:3 simulation:6 covariance:20 solid:3 crowding:1 reduction:1 configuration:6 series:1 contextual:14 activation:12 yet:3 intriguing:1 must:2 extraclassical:2 physiol:1 realistic:2 numerical:1 additive:1 predetermined:... |
3,084 | 3,795 | On Stochastic and Worst-case Models for Investing
Elad Hazan
IBM Almaden Research Center
650 Harry Rd, San Jose, CA 95120
ehazan@cs.princeton.edu
Satyen Kale
Yahoo! Research
4301 Great America Parkway, Santa Clara, CA 95054
skale@yahoo-inc.com
Abstract
In practice, most investing is done assuming a probabilistic mod... | 3795 |@word determinant:1 version:6 polynomial:1 norm:3 d2:2 calculus:1 incurs:1 dramatic:1 tr:1 initial:2 ftrl:1 series:10 selecting:1 ecole:1 past:2 kx0:1 current:1 com:1 ka:2 clara:1 yet:1 dx:1 written:2 ws1:1 benign:1 unchanging:1 update:1 v:16 devising:1 warmuth:1 ith:1 prize:1 short:1 manfred:1 math:1 successive:... |
3,085 | 3,796 | Linear-time Algorithms for Pairwise Statistical
Problems
Parikshit Ram, Dongryeol Lee, William B. March and Alexander G. Gray
Computational Science and Engineering, Georgia Institute of Technology
Atlanta, GA 30332
{p.ram@,dongryel@cc.,march@,agray@cc.}gatech.edu
Abstract
Several key computational bottlenecks in machi... | 3796 |@word version:1 simulation:8 decomposition:3 karger:3 hardy:1 existing:2 beygelzimer:8 must:1 john:1 distant:2 alone:1 implying:1 leaf:2 intelligence:2 ruhl:3 provides:2 math:1 node:19 constructed:1 become:1 symposium:3 descendant:3 prove:8 consists:1 inside:2 introduce:1 pairwise:7 inter:1 expected:2 rapid:1 beh... |
3,086 | 3,797 | Exploring Functional Connectivity of the Human
Brain using Multivariate Information Analysis
Barry Chai1?
Dirk B. Walther2?
Diane M. Beck2,3?
Li Fei-Fei1?
1
Computer Science Department, Stanford University, Stanford, CA 94305
2
Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801
3
Psychology... | 3797 |@word cox:2 briefly:1 faculty:1 version:1 mri:1 vi1:1 uncovers:1 accounting:1 reduction:3 liu:1 uncovered:1 loc:3 selecting:4 contains:1 united:1 hemodynamic:1 interestingly:3 outperforms:3 existing:2 comparing:2 activation:3 si:2 must:6 written:1 john:1 cruz:3 numerical:2 subsequent:1 informative:7 confirming:3 ... |
3,087 | 3,798 | Evaluating multi-class learning strategies in a
hierarchical framework for object detection
Sanja Fidler
Marko Boben
Ale?s Leonardis
Faculty of Computer and Information Science
University of Ljubljana, Slovenia
{sanja.fidler, marko.boben, ales.leonardis}@fri.uni-lj.si
Abstract
Multi-class object learning and detectio... | 3798 |@word middle:1 faculty:1 vi1:1 disk:6 seek:2 decomposition:1 q1:1 recursively:1 contains:4 fragment:7 score:2 fevrier:1 ours:1 interestingly:1 outperforms:2 existing:1 comparing:1 si:1 yet:2 must:1 fn:5 shape:34 treating:1 plot:3 progressively:1 depict:3 fund:1 alone:1 generative:5 selected:1 half:2 device:1 bart... |
3,088 | 3,799 | A Smoothed Approximate Linear Program
Vijay V. Desai
IEOR, Columbia University
vvd2101@columbia.edu
Vivek F. Farias
MIT Sloan
vivekf@mit.edu
Ciamac C. Moallemi
GSB, Columbia University
ciamac@gsb.columbia.edu
Abstract
We present a novel linear program for the approximation of the dynamic
programming cost-to-go funct... | 3799 |@word illustrating:2 version:6 polynomial:1 stronger:2 norm:3 seek:3 simulation:1 contraction:1 solid:1 harder:2 carry:1 initial:3 efficacy:1 selecting:1 score:1 cleared:1 outperforms:1 existing:1 must:1 readily:1 reminiscent:1 belmont:1 subsequent:1 analytic:1 designed:2 stationary:2 greedy:1 leaf:1 assurance:1 ... |
3,089 | 38 | 457
DISTRIBUTED NEURAL INFORMATION PROCESSING
IN THE VESTIBULO-OCULAR SYSTEM
Clifford Lau
Office of Naval Research Detach ment
Pasadena, CA 91106
Vicente Honrubia*
UCLA Division of Head and Neck Surgery
Los Angeles, CA 90024
ABSTRACT
A new distributed neural information-processing
model is proposed to explain the resp... | 38 |@word nd:1 sensed:1 innervating:1 shading:1 efficacy:2 tuned:1 anterior:4 surprising:1 must:1 interspike:1 pertinent:1 plot:2 discrimination:1 nervous:3 ith:1 smith:1 filtered:1 provides:1 location:5 behavior:2 innervation:3 increasing:1 project:1 medium:2 emerging:1 finding:3 um:1 schwartz:2 grant:2 thinner:1 tend... |
3,090 | 380 | A Novel Approach to Prediction of the
3-Dimensional Structures of Protein Backbones
by Neural Networks
Henrik Fredholrn l ,5
and
2
Henrik Bol1l' , Jakob Bohr 3 , S0ren Brunak4 ,
Rodney M.J. Cotterill\ Benny Lautrup 5 and Steffen B. Petersen l
1 MR-Senteret,
SINTEF, N-7034 Trondheim, Norway.
2University of Illinois, U... | 380 |@word version:1 seems:1 pancreatic:4 amply:1 initial:2 configuration:2 current:2 stemming:1 prk:1 depict:1 alone:1 half:1 guess:2 steepest:1 tertiary:4 short:1 contribute:2 simpler:1 along:2 beta:1 consists:1 fitting:1 ray:5 acquired:1 growing:1 steffen:1 encouraging:1 window:4 increasing:1 what:2 backbone:21 deve... |
3,091 | 3,800 | Exponential Family Graph Matching and Ranking
James Petterson, Tib?erio S. Caetano, Julian J. McAuley and Jin Yu
NICTA, Australian National University
Canberra, Australia
Abstract
We present a method for learning max-weight matching predictors in bipartite
graphs. The method consists of performing maximum a posterior... | 3800 |@word determinant:2 version:2 middle:1 exploitation:1 seems:2 open:1 fairer:1 attainable:1 solid:2 mcauley:1 liu:6 contains:2 score:5 xiy:12 series:1 hardy:2 document:36 existing:1 comparing:1 yet:1 must:1 readily:1 realistic:1 partition:11 numerical:1 shape:4 hofmann:1 drop:1 plot:3 resampling:1 selected:3 leaf:... |
3,092 | 3,801 | Sparse and Locally Constant Gaussian Graphical
Models
Jean Honorio, Luis Ortiz, Dimitris Samaras
Department of Computer Science
Stony Brook University
Stony Brook, NY 11794
{jhonorio,leortiz,samaras}@cs.sunysb.edu
Nikos Paragios
Laboratoire MAS
Ecole Centrale Paris
Chatenay-Malabry, France
nikos.paragios@ecp.fr
Rita G... | 3801 |@word determinant:2 version:1 mri:12 polynomial:1 norm:17 open:2 d2:6 propagate:1 covariance:18 contraction:2 natsoulis:1 minus:2 solid:1 initial:1 contains:5 selecting:1 ecole:1 outperforms:6 recovered:8 stony:2 written:1 luis:1 distant:3 remove:1 update:5 stationary:1 intelligence:3 colored:1 detecting:1 zhang:... |
3,093 | 3,802 | Speaker Comparison with Inner Product
Discriminant Functions
W. M. Campbell
MIT Lincoln Laboratory
Lexington, MA 02420
wcampbell@ll.mit.edu
Z. N. Karam
DSPG, MIT RLE, Cambridge MA
MIT Lincoln Laboratory, Lexington, MA
zahi@mit.edu
D. E. Sturim
MIT Lincoln Laboratory
Lexington, MA 02420
sturim@ll.mit.edu
Abstract
Spea... | 3802 |@word trial:1 eliminating:1 norm:4 vogt:1 supervectors:1 d2:18 linearized:1 covariance:8 decomposition:1 eng:5 fortuitous:1 reduction:2 moment:1 configuration:2 initial:2 score:2 united:1 interestingly:1 reynolds:2 existing:1 current:2 comparing:5 nt:2 lang:1 written:1 remove:2 moreno:1 sponsored:1 oblique:7 i100... |
3,094 | 3,803 | Optimal Scoring for Unsupervised Learning
Zhihua Zhang and Guang Dai
College of Computer Science & Technology
Zhejiang University
Hangzhou, Zhejiang, 310027 China
Abstract
We are often interested in casting classification and clustering problems as a regression framework, because it is feasible to achieve some statis... | 3803 |@word norm:1 duda:1 c0:11 d2:5 decomposition:1 tr:33 reduction:5 initial:1 configuration:1 series:2 rkhs:1 outperforms:1 existing:1 scatter:5 john:1 numerical:1 partition:4 depict:1 n0:1 intelligence:1 accordingly:1 provides:2 zhang:3 five:1 rc:2 c2:10 direct:1 x0:39 upenn:1 pkdd:1 multi:2 cardinality:2 increasin... |
3,095 | 3,804 | Multiple Incremental Decremental Learning of
Support Vector Machines
Masayuki Karasuyama and Ichiro Takeuchi
Department of Engineering, Nagoya Institute of Technology
Gokiso-cho, Syouwa-ku, Nagoya, Aichi, 466-8555, JAPAN
krsym@ics.nitech.ac.jp, takeuchi.ichiro@nitech.ac.jp
Abstract
We propose a multiple incremental d... | 3804 |@word briefly:2 norm:2 termination:1 solid:1 initial:4 series:3 contains:1 existing:1 must:4 written:1 numerical:1 enables:1 remove:12 plot:9 seeding:5 update:25 obsolete:1 oldest:1 ith:1 short:1 iterates:1 along:1 become:2 qij:15 interscience:1 inside:1 introduce:2 roughly:1 multi:7 cpu:12 decoste:3 cache:2 solv... |
3,096 | 3,805 | Variational Gaussian-process factor analysis for
modeling spatio-temporal data
Alexander Ilin
Adaptive Informatics Research Center
Helsinki University of Technology, Finland
Alexander.Ilin@tkk.fi
Jaakko Luttinen
Adaptive Informatics Research Center
Helsinki University of Technology, Finland
Jaakko.Luttinen@tkk.fi
Ab... | 3805 |@word trial:1 polynomial:3 loading:5 stronger:1 nd:3 covariance:19 decomposition:1 tr:6 solid:3 series:4 contains:5 existing:1 recovered:1 current:1 wx:1 update:9 generative:1 selected:4 guess:1 intelligence:4 isotropic:1 regressive:1 location:11 five:3 dn:4 ilin:4 consists:1 fitting:1 combine:1 interscience:1 in... |
3,097 | 3,806 | Gaussian process regression with Student-t likelihood
Pasi Jyl?anki
Department of Biomedical Engineering
and Computational Science
Helsinki University of Technology
Finland
pasi.jylanki@tkk.fi
Jarno Vanhatalo
Department of Biomedical Engineering
and Computational Science
Helsinki University of Technology
Finland
jarn... | 3806 |@word proportionality:1 vanhatalo:2 tried:1 covariance:14 decomposition:3 solid:1 harder:1 edric:1 ld:2 electronics:1 series:1 current:1 si:2 scatter:1 must:1 john:2 numerical:2 visible:1 happen:1 enables:2 plot:6 update:10 v:2 stationary:1 intelligence:1 selected:1 accordingly:1 beginning:2 ith:2 manfred:1 revis... |
3,098 | 3,807 | Tracking Dynamic Sources of Malicious Activity at
Internet-Scale
Shobha Venkataraman?, Avrim Blum? , Dawn Song? , Subhabrata Sen? , Oliver Spatscheck?
?
AT&T Labs ? Research {shvenk,sen,spatsch}@research.att.com
?
Carnegie Mellon University avrim@cs.cmu.edu
?
University of California, Berkeley dawnsong@cs.berkeley.edu
... | 3807 |@word version:3 contains:1 att:1 prefix:35 existing:2 current:2 com:2 si:3 must:4 happen:2 treating:1 plot:2 update:9 discovering:3 leaf:35 instantiate:1 warmuth:2 website:1 core:1 farther:1 short:1 mitigation:2 coarse:1 provides:1 node:66 characterization:1 attack:6 org:1 zhang:1 rc:2 along:1 enterprise:9 become... |
3,099 | 3,808 | An Additive Latent Feature Model for Transparent
Object Recognition
Mario Fritz
UC Berkeley
Gary Bradski
Willow Garage
Michael Black
Brown University
Sergey Karayev
UC Berkeley
Trevor Darrell
UC Berkeley
Abstract
Existing methods for visual recognition based on quantized local features can perform poorly when local ... | 3808 |@word dalal:1 proportion:4 advantageous:1 everingham:1 triggs:1 tried:1 decomposition:3 jacob:1 dramatic:1 shechtman:1 initial:1 ours:2 document:1 existing:2 current:2 com:1 activation:11 wherefore:1 additive:10 partition:2 informative:1 shape:6 hofmann:1 designed:2 v:1 generative:5 prohibitive:1 leaf:1 discoveri... |
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