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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3,400 | 4,079 | Decomposing Isotonic Regression for Efficiently
Solving Large Problems
Ronny Luss
Dept. of Statistics and OR
Tel Aviv University
ronnyluss@gmail.com
Saharon Rosset
Dept. of Statistics and OR
Tel Aviv University
saharon@post.tau.ac.il
Moni Shahar
Dept. of Electrical Eng.
Tel Aviv University
moni@eng.tau.ac.il
Abstra... | 4079 |@word trial:1 version:1 eliminating:2 polynomial:1 open:1 seek:1 simulation:3 eng:2 decomposition:7 jacob:1 recursively:2 reduction:6 initial:1 contains:1 series:1 leeuw:1 document:1 outperforms:1 current:1 com:2 si:2 gmail:1 yet:1 must:2 written:2 john:1 numerical:2 partition:12 realistic:1 update:3 aps:1 v:2 is... |
3,401 | 408 | ADAPTIVE SPLINE NETWORKS
Jerome H. Friedman
Department of Statistics and
Stanford Linear Accelerator Center
Stanford University
Stanford, CA 94305
Abstract
A network based on splines is described. It automatically adapts the number of units, unit parameters, and the architecture of the network for each
application.
... | 408 |@word briefly:1 polynomial:1 km:1 dramatic:1 thereby:1 moment:1 initial:2 series:2 realize:2 belmont:1 additive:11 happen:1 partition:3 enables:1 treating:1 interpretable:1 greedy:2 selected:9 provides:1 cheney:2 location:1 contribute:2 sigmoidal:2 five:2 along:2 fitting:1 manner:1 boor:1 nor:1 ol:1 terminal:2 bel... |
3,402 | 4,080 | Brain covariance selection: better individual
functional connectivity models using population prior
Ga?el Varoquaux?
Parietal, INRIA
NeuroSpin, CEA, France
gael.varoquaux@normalesup.org
Jean-Baptiste Poline
LNAO, I2BM, DSV
NeuroSpin, CEA, France
jbpoline@cea.fr
Alexandre Gramfort
Parietal, INRIA
NeuroSpin, CEA, Franc... | 4080 |@word middle:1 mri:2 norm:6 open:2 grey:1 lobe:2 covariance:25 mention:1 tr:2 carry:2 reduction:1 series:8 score:3 selecting:1 outperforms:1 current:2 comparing:1 activation:3 written:1 must:1 kiebel:1 distant:1 concatenate:1 partition:2 shape:1 motor:3 remove:1 reproducible:1 interpretable:2 designed:1 update:1 ... |
3,403 | 4,081 | Empirical Bernstein Inequalities for U-Statistics
Sandrine Anthoine
LATP, Aix-Marseille Universit?e, CNRS
39, rue F. Joliot Curie
F-13013 Marseille, France
anthoine@cmi.univ-mrs.fr
Thomas Peel
LIF, Aix-Marseille Universit?e
39, rue F. Joliot Curie
F-13013 Marseille, France
thomas.peel@lif.univ-mrs.fr
Liva Ralaivola
... | 4081 |@word cmi:1 version:4 briefly:1 arcones:4 nd:1 qsym:9 d2:1 simulation:1 bn:2 decomposition:1 mention:2 carry:3 score:4 bc:1 existing:1 current:1 z2:2 tackling:1 liva:2 plot:3 drop:1 update:2 designed:2 v:1 half:2 prohibitive:1 rudin:1 ntrain:4 xk:2 yi1:1 anthoine:2 lr:1 characterization:1 provides:4 boosting:1 he... |
3,404 | 4,082 | Copula Processes
Andrew Gordon Wilson?
Department of Engineering
University of Cambridge
agw38@cam.ac.uk
Zoubin Ghahramani?
Department of Engineering
University of Cambridge
zoubin@eng.cam.ac.uk
Abstract
We define a copula process which describes the dependencies between arbitrarily
many random variables independent... | 4082 |@word version:2 replicate:1 bf:2 open:1 km:1 vanhatalo:2 simulation:4 eng:2 covariance:16 decomposition:1 fifteen:1 series:3 contains:1 daniel:1 past:2 outperforms:2 current:1 ka:1 comparing:2 elliptical:2 com:2 torben:1 guez:2 must:1 john:1 fn:8 numerical:1 happen:1 christian:1 designed:1 update:8 stationary:2 l... |
3,405 | 4,083 | Batch Bayesian Optimization
via Simulation Matching
Javad Azimi, Alan Fern, Xiaoli Z. Fern
School of EECS, Oregon State University
{azimi, afern, xfern}@eecs.oregonstate.edu
Abstract
Bayesian optimization methods are often used to optimize unknown functions that
are costly to evaluate. Typically, these methods sequen... | 4083 |@word exploitation:1 pcc:1 norm:1 nd:1 simulation:16 tried:1 covariance:2 reduction:1 initial:4 contains:2 selecting:13 denoting:1 genetic:1 interestingly:1 outperforms:2 existing:1 freitas:1 current:6 si:21 must:5 written:1 realize:1 remove:2 plot:3 update:1 v:2 greedy:11 selected:9 accordingly:1 xk:2 ith:1 rose... |
3,406 | 4,084 | Online Learning in the Manifold of
Low-Rank Matrices
Uri Shalit?, Daphna Weinshall
Computer Science Dept. and ICNC
The Hebrew University of Jerusalem
uri.shalit@mail.huji.ac.il
daphna@cs.huji.ac.il
Gal Chechik
Google Research and
The Gonda Brain Research Center
Bar Ilan University
gal@google.com
Abstract
When learnin... | 4084 |@word multitask:1 kulis:3 inversion:1 norm:5 nd:1 dekel:1 crucially:1 decomposition:3 infogain:1 minus:1 sepulchre:2 bai:1 score:7 document:15 past:1 outperforms:1 current:2 com:1 z2:9 lang:1 stemming:1 numerical:1 informative:1 enables:1 remove:1 plot:1 update:9 intelligence:1 selected:9 yr:1 parametrization:1 s... |
3,407 | 4,085 | Why are some word orders more common than
others? A uniform information density account
Luke Maurits, Amy Perfors & Daniel Navarro
School of Psychology,
University of Adelaide,
Adelaide, South Australia, 5000
{luke.maurits, amy.perfors, daniel.navarro}@adelaide.edu.au
Abstract
Languages vary widely in many ways, incl... | 4085 |@word version:3 judgement:4 proportion:1 norm:1 elly:1 seems:1 cola:1 seek:1 contraction:2 simplifying:2 prominence:1 carry:4 reduction:4 initial:1 score:14 charniak:1 daniel:4 genetic:4 o2:12 current:1 recovered:2 com:2 must:4 written:1 subsequent:1 happen:1 informative:1 blur:1 shape:2 designed:1 childes:3 v:2 ... |
3,408 | 4,086 | Linear Complementarity for Regularized Policy
Evaluation and Improvement
Jeff Johns
Christopher Painter-Wakefield
Department of Computer Science
Duke University
Durham, NC 27708
Ronald Parr
{johns, paint007, parr}@cs.duke.edu
Abstract
Recent work in reinforcement learning has emphasized the power of L1 regularizat... | 4086 |@word trial:3 norm:1 stronger:1 termination:2 seek:1 tried:1 decomposition:1 pick:1 thereby:2 reduction:1 initial:2 series:1 selecting:1 current:5 comparing:1 si:2 must:10 written:2 john:4 ronald:1 plot:2 update:1 greedy:19 selected:1 guess:2 fewer:3 parameterization:2 provides:4 matrix1:1 redone:1 characterizati... |
3,409 | 4,087 | Extended Bayesian Information Criteria for Gaussian
Graphical Models
Mathias Drton
University of Chicago
drton@uchicago.edu
Rina Foygel
University of Chicago
rina@uchicago.edu
Abstract
Gaussian graphical models with sparsity in the inverse covariance matrix are of
significant interest in many modern applications. Fo... | 4087 |@word trial:1 determinant:1 version:4 stronger:1 seems:1 norm:1 proportionality:1 open:1 simulation:8 bn:1 covariance:10 contains:1 series:2 score:1 recovered:2 comparing:2 must:1 readily:1 numerical:1 chicago:2 alone:1 implying:1 selected:2 node:11 firstly:1 along:2 beta:2 prove:1 introduce:1 theoretically:1 ind... |
3,410 | 4,088 | Hashing Hyperplane Queries to Near Points
with Applications to Large-Scale Active Learning
Sudheendra Vijayanarasimhan
Department of Computer Science
University of Texas at Austin
svnaras@cs.utexas.edu
Prateek Jain
Algorithms Research Group
Microsoft Research, Bangalore, India
prajain@microsoft.com
Kristen Grauman
D... | 4088 |@word kulis:1 illustrating:1 version:1 briefly:1 compression:1 norm:5 stronger:5 hu:15 d2:4 seek:1 cos2:3 zelnik:1 decomposition:1 accounting:1 incurs:1 thereby:2 tr:2 reduction:1 initial:1 liu:1 series:1 selecting:1 ours:1 document:3 existing:6 current:5 com:1 yet:4 must:3 realistic:1 subsequent:1 informative:2 ... |
3,411 | 4,089 | Learning to combine foveal glimpses with a
third-order Boltzmann machine
Hugo Larochelle and Geoffrey Hinton
Department of Computer Science, University of Toronto
6 King?s College Rd, Toronto, ON, Canada, M5S 3G4
{larocheh,hinton}@cs.toronto.edu
Abstract
We describe a model based on a Boltzmann machine with third-orde... | 4089 |@word version:1 middle:1 advantageous:1 nd:1 adrian:1 hyv:1 contrastive:3 tr:1 reduction:2 initial:1 liu:1 foveal:2 series:1 score:5 contains:1 tuned:2 current:4 contextual:1 activation:1 yet:1 must:6 written:1 john:1 cottrell:1 visible:7 concatenate:1 j1:3 wx:1 shape:7 informative:1 utml:1 remove:1 treating:1 de... |
3,412 | 4,090 | Interval Estimation for Reinforcement-Learning
Algorithms in Continuous-State Domains
Adam White
Department of Computing Science
University of Alberta
awhite@cs.ualberta.ca
Martha White
Department of Computing Science
University of Alberta
whitem@cs.ualberta.ca
Abstract
The reinforcement learning community has explo... | 4090 |@word mild:1 trial:1 exploitation:1 illustrating:1 innovates:1 achievable:1 interleave:1 polynomial:1 nd:1 glue:2 twelfth:1 turlach:1 termination:3 gptd:1 bn:1 simplifying:2 covariance:4 prasad:1 q1:1 moment:1 initial:1 series:4 selecting:5 daniel:1 tuned:1 bootstrapped:5 interestingly:1 past:1 current:2 yet:1 mu... |
3,413 | 4,091 | Active Learning Applied to Patient-Adaptive
Heartbeat Classification
John V. Guttag
CSAIL, MIT
guttag@csail.mit.edu
Jenna Wiens
CSAIL, MIT
jwiens@csail.mit.edu
Abstract
While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had lim... | 4091 |@word mild:1 longterm:1 e215:2 prognostic:1 open:1 hu:2 contraction:3 decomposition:1 harder:1 initial:5 contains:3 series:2 score:9 selecting:1 outperforms:5 current:3 com:1 goldberger:1 john:1 fn:3 benign:1 hypothesize:1 designed:3 plot:1 v:5 alone:1 half:2 pacemaker:2 advancement:2 selected:4 fewer:3 spec:1 re... |
3,414 | 4,092 | Structural epitome: A way to summarize one?s visual
experience
Nebojsa Jojic
Microsoft Research
Alessandro Perina
Microsoft Research
University of Verona
Vittorio Murino
Italian Institute of Technology
University of Verona
Abstract
In order to study the properties of total visual input in humans, a single subject
w... | 4092 |@word version:2 verona:2 decomposition:1 dramatic:1 configuration:1 contains:3 score:1 outperforms:1 existing:1 com:2 si:30 yet:2 must:1 readily:1 written:1 numerical:6 partition:1 shape:1 update:8 nebojsa:1 generative:3 half:1 device:1 parametrization:1 provides:1 quantized:1 contribute:1 location:12 codebook:1 ... |
3,415 | 4,093 | Deciphering subsampled data: adaptive compressive
sampling as a principle of brain communication
Guy Isely
Redwood Center for Theoretical Neuroscience
University of California, Berkeley
guyi@berkeley.edu
Christopher J. Hillar
Mathematical Sciences Research Institute
chillar@msri.org
Friedrich T. Sommer
University of... | 4093 |@word mild:2 achievable:1 compression:16 seems:1 simulation:1 bn:1 decomposition:1 citeseer:2 asks:1 tuned:1 interestingly:2 outperforms:1 current:1 recovered:4 intriguing:2 dct:11 distant:1 plasticity:1 shape:2 motor:1 hypothesize:1 remove:1 plot:1 v:3 generative:1 fewer:3 half:1 nervous:1 smith:1 completeness:1... |
3,416 | 4,094 | Word Features for Latent Dirichlet Allocation
James Petterson1, Alex Smola2, Tiberio Caetano1, Wray Buntine1, Shravan Narayanamurthy3
1
NICTA and ANU, Canberra, ACT, Australia
2
Yahoo! Research, Santa Clara, CA, USA
3
Yahoo! Research, Bangalore, India
Abstract
We extend Latent Dirichlet Allocation (LDA) by explicitly ... | 4094 |@word briefly:1 proportion:1 open:1 d2:7 km:7 hu:1 pick:1 initial:1 liu:1 contains:1 score:1 daniel:1 document:46 existing:2 clara:1 yet:1 portuguese:25 john:1 stemming:1 subsequent:1 additive:1 numerical:1 kdd:1 shape:1 treating:1 plot:2 update:1 resampling:4 generative:1 half:2 selected:2 intelligence:2 leaf:1 ... |
3,417 | 4,095 | Learning Multiple Tasks with a Sparse
Matrix-Normal Penalty
Yi Zhang
Machine Learning Department
Carnegie Mellon University
yizhang1@cs.cmu.edu
Jeff Schneider
The Robotics Institute
Carnegie Mellon University
schneide@cs.cmu.edu
Abstract
In this paper, we propose a matrix-variate normal penalty with sparse inverse c... | 4095 |@word multitask:2 determinant:3 norm:3 annoying:1 covariance:57 decomposition:1 jacob:1 tr:10 moment:1 configuration:2 contains:3 score:4 series:1 liu:1 nt:5 chu:1 must:1 numerical:2 enables:1 laplacianfaces:7 discovering:2 prohibitive:1 selected:1 ith:1 detecting:3 provides:5 node:1 preference:1 zhang:4 along:1 ... |
3,418 | 4,096 | The LASSO risk: asymptotic results and real world
examples
Mohsen Bayati
Stanford University
bayati@stanford.edu
Jos?e Bento
Stanford University
jbento@stanford.edu
Andrea Montanari
Stanford University
montanar@stanford.edu
Abstract
We consider the problem of learning a coefficient vector x0 ? RN from noisy
linear ... | 4096 |@word version:2 pw:2 norm:3 owlqn:1 hu:1 simulation:7 seek:1 moment:2 initial:1 series:1 denoting:1 amp:8 ka:2 universality:2 dx:1 portuguese:1 numerical:4 happen:1 shamai:1 plot:6 greedy:1 selected:2 fpr:6 record:6 foreseeable:1 provides:3 characterization:3 ct07:4 node:1 complication:1 location:1 org:3 simpler:... |
3,419 | 4,097 | Hallucinations in Charles Bonnet Syndrome Induced
by Homeostasis: a Deep Boltzmann Machine Model
David P. Reichert, Peggy Series and Amos J. Storkey
School of Informatics, University of Edinburgh
10 Crichton Street, Edinburgh, EH8 9AB
{d.p.reichert@sms., pseries@inf., a.storkey@} ed.ac.uk
Abstract
The Charles Bonnet ... | 4097 |@word trial:2 blindness:1 houweling:1 contrastive:1 initial:4 cyclic:1 series:1 ours:1 blank:15 current:2 activation:12 yet:1 intriguing:1 realize:1 subsequent:1 visible:7 wx:1 plasticity:3 shape:9 v:2 generative:7 half:10 greedy:1 intelligence:1 xk:3 core:1 short:1 mental:7 contribute:1 location:2 simpler:1 dn:1... |
3,420 | 4,098 | Nonparametric Density Estimation for Stochastic
Optimization with an Observable State Variable
Lauren A. Hannah
Duke University
Durham, NC 27701
lh140@duke.edu
Warren B. Powell
Princeton University
Princeton, NJ 08544
powell@princeton.edu
David M. Blei
Princeton University
Princeton, NJ 08544
blei@cs.princeton.edu
... | 4098 |@word polynomial:2 proportion:3 simulation:2 pulse:1 profit:1 accommodate:2 contains:2 series:3 past:1 existing:2 current:6 unction:4 si:23 dx:1 must:5 john:2 fn:2 partition:15 shape:2 treating:1 selected:2 accordingly:1 xk:5 fa9550:1 blei:3 math:1 contribute:1 location:3 simpler:2 mathematical:3 along:1 construc... |
3,421 | 4,099 | Sparse Inverse Covariance Selection via
Alternating Linearization Methods
Katya Scheinberg
Department of ISE
Lehigh University
katyas@lehigh.edu
Shiqian Ma, Donald Goldfarb
Department of IEOR
Columbia University
{sm2756,goldfarb}@columbia.edu
Abstract
Gaussian graphical models are of great interest in statistical le... | 4099 |@word determinant:1 briefly:1 version:7 polynomial:1 seek:2 covariance:24 decomposition:5 pick:3 harder:1 ipm:4 reduction:2 initial:2 liu:1 contains:1 zij:3 pub:1 outperforms:3 existing:1 rish:3 current:2 recovered:1 skipping:2 optim:2 toh:5 yet:1 written:2 numerical:7 kdd:1 update:8 v:6 greedy:2 intelligence:1 g... |
3,422 | 41 | 814
NEUROMORPHIC NETWORKS BASED
ON SPARSE OPTICAL ORTHOGONAL CODES
Mario P. Vecchi and Jawad A. Salehi
Bell Communications Research
435 South Street
Morristown, NJ 07960-1961
Abstrad
A family of neuromorphic networks specifically designed for communications
and optical signal processing applications is presented. The... | 41 |@word version:2 simulation:6 simplifying:1 configuration:1 contains:4 substitution:1 comparing:1 cad:2 written:2 must:1 distant:1 happen:1 wanted:1 designed:4 v_:3 selected:1 device:2 item:2 ria:1 ith:2 node:9 differential:1 salehi:4 mask:6 expected:2 alspector:1 themselves:1 growing:1 lll:1 project:1 circuit:1 med... |
3,423 | 410 | Comparison of three classification techniques,
CART, C4.5 and Multi-Layer Perceptrons
A C Tsoi
R A Pearson
Department of Electrical EngineeringDepartment of Computer Science
University of Queensland
Aust Defence Force Academy
St Lucia, Queensland 4072
Campbell, ACT 2600
Australia
Australia
Abstract
In this paper, aft... | 410 |@word version:5 fairer:1 queensland:2 mention:1 carry:1 initial:2 charniak:2 tuned:4 past:1 comparing:1 yet:2 must:4 predetermined:1 designed:2 atlas:4 progressively:1 pursued:2 leaf:1 intelligence:3 beginning:1 consulting:1 equi:1 node:1 sigmoidal:1 popularised:1 qualitative:2 introductory:1 nor:2 frequently:1 mu... |
3,424 | 4,100 | Global seismic monitoring as probabilistic inference
Nimar S. Arora
Department of Computer Science
University of California, Berkeley
Berkeley, CA 94720
nimar@cs.berkeley.edu
Stuart Russell
Department of Computer Science
University of California, Berkeley
Berkeley, CA 94720
russell@cs.berkeley.edu
Paul Kidwell
Lawre... | 4100 |@word aircraft:1 compression:1 norm:1 km:4 additively:1 hu:1 pick:2 thereby:1 score:16 united:4 daniel:1 existing:1 err:4 current:8 comparing:2 ronan:1 subsequent:1 informative:1 enables:1 remove:1 treating:1 mackey:1 generative:5 half:1 advancement:1 greedy:2 ith:1 core:2 short:1 record:1 footing:1 location:21 a... |
3,425 | 4,101 | Towards Property-Based Classification of Clustering
Paradigms
Margareta Ackerman, Shai Ben-David, and David Loker
D.R.C. School of Computer Science
University of Waterloo, Canada
{mackerma, shai, dloker}@cs.uwaterloo.ca
Abstract
Clustering is a basic data mining task with a wide variety of applications. Not
surprisin... | 4101 |@word illustrating:1 version:1 seems:2 d2:5 pick:1 initial:1 selecting:2 denoting:1 existing:1 dx:6 must:1 readily:1 partition:7 v:1 intelligence:1 xk:8 affair:1 iso:2 characterization:4 bijection:2 preference:4 simpler:2 zhang:2 mathematical:1 c2:2 manner:2 pairwise:1 expected:1 indeed:2 behavior:5 examine:1 lit... |
3,426 | 4,102 | Collaborative Filtering in a Non-Uniform World:
Learning with the Weighted Trace Norm
Ruslan Salakhutdinov
Brain and Cognitive Sciences and CSAIL, MIT
Cambridge, MA 02139
rsalakhu@mit.edu
Nathan Srebro
Toyota Technological Institute at Chicago
Chicago, Illinois 60637
nati@ttic.edu
Abstract
We show that matrix comple... | 4102 |@word middle:3 version:3 stronger:2 norm:96 seems:2 confirms:1 simulation:3 r:1 decomposition:1 tr:1 solid:4 liu:1 contains:1 ntc:1 selecting:1 interestingly:1 outperforms:1 current:1 comparing:1 yet:1 must:2 written:1 chicago:2 analytic:2 kyb:4 update:1 aside:2 half:3 selected:4 fewer:1 characterization:1 provid... |
3,427 | 4,103 | Extensions of Generalized Binary Search to Group
Identification and Exponential Costs
Gowtham Bellala1 , Suresh K. Bhavnani2,3,4 , Clayton Scott1
Department of EECS, University of Michigan, Ann Arbor, MI 48109
2
Institute for Translational Sciences, 3 Dept. of Preventative Medicine and Community Health,
University of ... | 4103 |@word version:1 proportion:1 unif:1 open:1 simulation:1 q1:4 reduction:12 prefix:2 ka:14 com:1 gmail:1 john:1 partition:1 greedy:20 leaf:10 intelligence:1 provides:1 coarse:1 node:57 allerton:1 org:3 mathematical:1 along:1 constructed:5 beta:7 przytycka:1 symposium:1 persistent:1 consists:1 prove:1 ray:1 manner:1... |
3,428 | 4,104 | Size Matters: Metric Visual Search Constraints from
Monocular Metadata
Mario Fritz
UC Berkeley EECS & ICSI
Kate Saenko
UC Berkeley EECS & ICSI
Trevor Darrell
UC Berkeley EECS & ICSI
Abstract
Metric constraints are known to be highly discriminative for many objects, but
if training is limited to data captured from a ... | 4104 |@word exploitation:1 trigonometry:1 shot:1 shechtman:1 liu:2 contains:2 score:4 hoiem:1 kleenex:1 existing:1 com:2 z2:1 yet:1 wherefore:1 readily:1 john:1 happen:1 informative:1 hofmann:1 shape:6 designed:1 gist:1 depict:1 v:1 alone:1 cue:3 leaf:1 device:2 item:1 intelligence:2 core:1 record:1 provides:2 node:8 l... |
3,429 | 4,105 | Simultaneous Object Detection and Ranking with
Weak Supervision
Matthew B. Blaschko
Andrea Vedaldi
Andrew Zisserman
Department of Engineering Science
University of Oxford
United Kingdom
Abstract
A standard approach to learning object category detectors is to provide strong supervision in the form of a region of in... | 4105 |@word version:2 dalal:3 proportion:3 everingham:3 triggs:3 carry:1 contains:4 score:12 united:1 hoiem:1 freitas:1 comparing:2 kdd:1 shape:1 hofmann:5 treating:2 v:2 intelligence:1 plane:2 es:1 boosting:2 location:12 org:1 constructed:1 direct:1 supply:1 consists:1 combine:1 excellence:1 roughly:1 behavior:1 andre... |
3,430 | 4,106 | Minimum Average Cost Clustering
Kiyohito Nagano
Institute of Industrial Science
University of Tokyo, Japan
nagano@sat.t.u-tokyo.ac.jp
Yoshinobu Kawahara
The Institute of Scientific and Industrial Research
Osaka University, Japan
kawahara@ar.sanken.osaka-u.ac.jp
Satoru Iwata
Research Institute for Mathematical Scienc... | 4106 |@word repository:1 polynomial:14 seems:1 q1:6 asks:2 initial:1 psj:3 existing:4 bd:2 subsequent:1 partition:82 designed:2 greedy:8 selected:1 intelligence:1 accordingly:1 attack:1 mathematical:4 symposium:2 consists:1 combine:1 manner:1 introduce:3 x0:5 inter:1 hardness:2 examine:1 automatically:1 resolve:1 becom... |
3,431 | 4,107 | Implicit Differentiation by Perturbation
Justin Domke
Rochester Institute of Technology
justin.domke@rit.edu
Abstract
This paper proposes a simple and efficient finite difference method for implicit differentiation of marginal inference results in discrete graphical models. Given an arbitrary loss function, defined o... | 4107 |@word mild:1 version:4 briefly:1 inversion:2 stronger:1 open:1 calculus:1 crucially:1 serafim:1 pick:1 kappen:1 initial:1 configuration:1 existing:2 recovered:2 comparing:1 written:2 must:1 bd:3 john:1 numerical:1 partition:3 sanjiv:2 enables:1 update:2 aside:1 half:1 selected:1 generative:1 amir:2 slowing:1 core... |
3,432 | 4,108 | Tree-Structured Stick Breaking for Hierarchical Data
Ryan Prescott Adams?
Dept. of Computer Science
University of Toronto
Zoubin Ghahramani
Dept. of Engineering
University of Cambridge
Michael I. Jordan
Depts. of EECS and Statistics
University of California, Berkeley
Abstract
Many data are naturally modeled by an u... | 4108 |@word multitask:1 version:3 interleave:1 proportion:1 seems:1 nd:1 covariance:2 initial:1 contains:1 selecting:1 united:1 daniel:3 document:15 kurt:1 existing:1 current:6 assigning:1 must:4 written:1 kamimura:1 partition:17 shape:1 update:1 bart:1 intelligence:2 discovering:1 leaf:1 fewer:1 warmuth:1 ith:1 hamilt... |
3,433 | 4,109 | Movement extraction by detecting
dynamics switches and repetitions
Silvia Chiappa
Statistical Laboratory
Wilberforce Road, Cambridge, UK
silvia@statslab.cam.ac.uk
Jan Peters
Max Planck Institute for Biological Cybernetics
Spemannstrasse 38, Tuebingen, Germany
jan.peters@tuebingen.mpg.de
Abstract
Many time-series suc... | 4109 |@word version:1 advantageous:1 c0:3 sex:1 accounting:1 covariance:1 versatile:1 initial:1 series:30 contains:2 current:2 com:1 must:1 subsequent:2 shape:1 motor:1 update:7 generative:1 device:5 advancement:1 beginning:3 detecting:2 node:2 org:1 simpler:1 become:2 consists:1 introduce:2 mpg:1 automatically:2 littl... |
3,434 | 411 | Simulation of the Neocognitron on a CCD
Parallel Processing Architecture
Michael L. Chuang and Alice M. Chiang
M.I.T Lincoln Laboratory
Lexington, MA 02173
e-mail: chuang@micro.ll.mit.edu
Abstract
The neocognitron is a neural network for pattern recognition and feature
extraction. An analog CCD parallel processing ar... | 411 |@word deformed:1 simulation:6 accommodate:1 reduction:1 initial:3 contains:2 tuned:1 lang:1 assigning:1 must:1 numerical:1 update:3 selected:1 device:9 plane:24 inspection:1 prespecified:1 chiang:7 quantized:3 contribute:1 node:11 toronto:1 along:1 consists:3 isscc:1 interlayer:1 td:1 window:3 notation:1 matched:1... |
3,435 | 4,110 | An Inverse Power Method for Nonlinear
Eigenproblems with Applications in
1-Spectral Clustering and Sparse PCA
?
Matthias Hein
Thomas Buhler
Saarland University, Saarbr?ucken, Germany
{hein,tb}@cs.uni-saarland.de
Abstract
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems... | 4110 |@word cu:4 middle:1 version:2 norm:5 loading:1 nd:2 hu:1 covariance:1 sepulchre:1 ipm:19 f0k:1 reduction:1 interestingly:1 recovered:2 current:1 yet:1 john:1 partition:4 plot:1 greedy:2 characterization:2 completeness:1 provides:1 unbounded:1 saarland:3 mathematical:2 constructed:1 direct:4 differential:1 prove:1... |
3,436 | 4,111 | Large-Scale Matrix Factorization with Missing Data
under Additional Constraints
Kaushik Mitra ??
Department of Electrical and Computer Engineering and UMIACS
University of Maryland, College Park, MD 20742
kmitra@umiacs.umd.edu
Sameer Sheorey?
Toyota Technological Institute, Chicago
ssameer@ttic.edu
Rama Chellappa
Depar... | 4111 |@word trial:6 briefly:1 version:1 norm:3 decomposition:2 jacob:1 euclidian:1 series:1 shum:1 current:1 recovered:1 numerical:1 additive:1 visible:1 chicago:1 shape:3 enables:1 remove:1 v:7 rrt:12 fewer:7 core:1 provides:1 gpca:1 brandt:1 mathematical:2 direct:1 ijcv:4 fitting:2 paragraph:1 buchanan:1 introduce:3 ... |
3,437 | 4,112 | Feature Transitions with Saccadic Search:
Size, Color, and Orientation Are Not Alike
Stella X. Yu
Computer Science Department
Boston College
Chestnut Hill, MA 02467
stella.yu@bc.edu
Abstract
Size, color, and orientation have long been considered elementary features whose
attributes are extracted in parallel and avail... | 4112 |@word trial:6 blindness:3 middle:2 proportion:2 nd:3 disk:72 confirms:1 crowding:8 extrastriate:1 contains:2 foveal:1 loc:6 bc:1 reaction:7 blank:9 current:2 contextual:1 must:2 attracted:1 ronald:1 shape:2 half:3 cue:1 item:6 beginning:1 short:1 coarse:1 quantized:1 location:9 preference:4 zhang:1 dell:1 along:2... |
3,438 | 4,113 | On the Convexity of Latent Social Network Inference
Jure Leskovec
Department of Computer Science
Stanford University
jure@cs.stanford.edu
Seth A. Myers
Institute for Computational
and Mathematical Engineering
Stanford University
samyers@stanford.edu
Abstract
In many real-world scenarios, it is nearly impossible to c... | 4113 |@word kong:1 middle:1 nd:1 tedious:1 sex:1 propagate:1 meansquare:1 covariance:1 series:3 contains:2 selecting:3 score:1 ours:1 rightmost:1 past:1 horvitz:1 recovered:1 current:1 activation:1 si:7 must:2 visible:1 happen:1 realistic:1 timestamps:1 kdd:2 remove:1 plot:3 v:2 generative:1 leaf:1 half:1 website:1 ith... |
3,439 | 4,114 | Slice sampling covariance hyperparameters
of latent Gaussian models
Ryan Prescott Adams
Dept. Computer Science
University of Toronto
Iain Murray
School of Informatics
University of Edinburgh
Abstract
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. ... | 4114 |@word mild:1 cox:3 version:2 confirms:1 simulation:2 crucially:1 covariance:30 decomposition:2 minus:1 moment:3 initial:3 configuration:1 contains:1 series:2 tuned:1 existing:3 imaginary:1 elliptical:4 current:17 com:1 must:5 john:1 informative:1 cheap:1 treating:1 designed:1 update:14 alone:1 generative:5 intell... |
3,440 | 4,115 | Supervised Clustering
Reza Bosagh Zadeh
Stanford University
rezab@stanford.edu
Pranjal Awasthi
Carnegie Mellon University
pawasthi@cs.cmu.edu
Abstract
Despite the ubiquity of clustering as a tool in unsupervised learning, there is not
yet a consensus on a formal theory, and the vast majority of work in this directio... | 4115 |@word middle:2 version:4 open:1 pick:1 asks:1 contains:7 document:7 current:1 yet:2 issuing:2 must:7 john:1 realistic:1 remove:3 v:1 half:10 prohibitive:1 intelligence:1 characterization:2 provides:2 node:1 preference:1 hyperplanes:3 simpler:2 mathematical:1 along:1 c2:1 symposium:4 incorrect:2 prove:2 focs:1 ins... |
3,441 | 4,116 | Online Learning: Random Averages, Combinatorial
Parameters, and Learnability
Alexander Rakhlin
Department of Statistics
University of Pennsylvania
Karthik Sridharan
Toyota Technological Institute
at Chicago
Ambuj Tewari
Computer Science Department
University of Texas at Austin
Abstract
We develop a theory of online... | 4116 |@word version:5 norm:5 nd:2 open:1 d2:3 contraction:2 prokhorov:1 q1:2 pick:2 mention:2 series:2 chervonenkis:3 discretization:3 written:1 john:1 chicago:1 enables:1 update:1 leaf:2 accordingly:1 lr:1 characterization:1 provides:3 math:1 simpler:2 mathematical:1 along:4 prove:5 introduce:2 indeed:1 expected:1 sub... |
3,442 | 4,117 | PAC-Bayesian Model Selection
for Reinforcement Learning
Joelle Pineau
School of Computer Science
McGill University
Montreal, Canada
jpineau@cs.mcgill.ca
Mahdi Milani Fard
School of Computer Science
McGill University
Montreal, Canada
mmilan1@cs.mcgill.ca
Abstract
This paper introduces the first set of PAC-Bayesian bo... | 4117 |@word exploitation:2 version:2 polynomial:3 norm:3 nd:2 contraction:2 initial:8 series:1 mmilan1:1 outperforms:2 current:1 shawetaylor:1 informative:9 shape:1 update:1 greedy:1 selected:1 guess:1 nq:1 intelligence:1 beginning:1 provides:2 mannor:1 herbrich:1 along:2 c2:8 farahmand:1 prove:3 combine:1 inside:1 int... |
3,443 | 4,118 | Occlusion Detection and Motion Estimation
with Convex Optimization
Alper Ayvaci,
Michalis Raptis,
Stefano Soatto
University of California, Los Angeles
{ayvaci, mraptis, soatto}@cs.ucla.edu
Abstract
We tackle the problem of simultaneously detecting occlusions and estimating optical flow. We show that, under standar... | 4118 |@word trial:1 middle:1 version:1 advantageous:1 norm:5 hu:3 citeseer:1 ronchetti:1 brightness:1 initial:1 series:1 score:2 disparity:1 nesta:1 shum:1 ours:3 ala:1 existing:1 ka:2 comparing:1 dx:2 written:1 must:1 finest:3 e01:2 john:1 visible:8 additive:2 numerical:1 benign:1 shape:2 visibility:2 kv1:4 strecha:1 ... |
3,444 | 4,119 | b-Bit Minwise Hashing for Estimating Three-Way Similarities
Ping Li
Dept. of Statistical Science
Cornell University
Arnd Christian K?onig
Microsoft Research
Microsoft Corporation
Wenhao Gui
Dept. of Statistical Science
Cornell University
Abstract
Computing1 two-way and multi-way set similarities is a fundamental pr... | 4119 |@word version:2 compression:2 norm:1 r13:4 disk:1 widom:1 willing:1 confirms:1 simulation:3 zelnik:1 vldb:1 covariance:1 eng:1 solid:4 reduction:2 initial:1 liu:1 contains:4 tuned:1 document:8 interestingly:2 bc:2 current:1 ganti:2 si:2 clara:1 conjunctive:1 crawling:2 must:1 suermondt:1 kdd:2 christian:1 plot:1 ... |
3,445 | 412 | Computing with Arrays of Bell-Shaped and
Sigmoid Functions
Pierre Baldi?
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109
Abstract
We consider feed-forward neural networks with one non-linear hidden layer
and linear output units. The transfer function in the hidden layer are either bell... | 412 |@word trial:1 briefly:3 polynomial:11 suitably:1 seek:1 simulation:1 bn:6 paid:1 initial:6 series:2 tuned:2 franklin:2 varx:1 universality:1 dx:6 must:1 written:1 john:5 numerical:1 partition:1 shape:2 enables:1 analytic:2 girosi:1 progressively:1 xk:1 weierstrass:2 provides:1 location:2 mathematical:2 along:1 dif... |
3,446 | 4,120 | Estimating Spatial Layout of Rooms using Volumetric
Reasoning about Objects and Surfaces
David C. Lee, Abhinav Gupta, Martial Hebert, Takeo Kanade
Carnegie Mellon University
{dclee,abhinavg,hebert,tk}@cs.cmu.edu
Abstract
There has been a recent push in extraction of 3D spatial layout of scenes. However,
none of these... | 4120 |@word manageable:1 stronger:1 tried:1 decomposition:1 solid:4 configuration:49 series:1 score:5 hoiem:3 renewed:1 ours:2 o2:1 existing:2 quadrilateral:2 current:2 recovered:1 outperforms:1 must:1 takeo:1 visible:1 shape:1 hofmann:1 hypothesize:1 cue:4 selected:1 yr:3 greedy:1 plane:7 vanishing:8 coughlan:1 colore... |
3,447 | 4,121 | Decoding Ipsilateral Finger Movements from ECoG
Signals in Humans
Yuzong Liu1 , Mohit Sharma2 , Charles M. Gaona2 , Jonathan D. Breshears3 , Jarod Roland 3 ,
Zachary V. Freudenburg1 , Kilian Q. Weinberger1 , and Eric C. Leuthardt2,3
1
Department of Computer Science and Engineering, Washington University in St. Louis
2... | 4121 |@word multitask:16 trial:3 neurophysiology:10 middle:6 norm:6 approved:1 seems:1 open:1 termination:1 confirms:1 vanhatalo:1 seitz:1 lobe:1 pick:3 carry:1 reduction:2 series:2 contains:1 united:1 past:1 existing:1 recovered:1 protection:1 activation:4 tetraplegic:1 subsequent:2 kdd:1 motor:50 designed:1 interpret... |
3,448 | 4,122 | Unsupervised Kernel Dimension Reduction
Meihong Wang
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
meihongw@usc.edu
Fei Sha
Dept. of Computer Science
U. of Southern California
Los Angeles, CA 90089
feisha@usc.edu
Michael I. Jordan
Dept. of Statistics
U. of California
Berkeley, CA
jordan@c... | 4122 |@word version:1 briefly:1 inversion:1 compression:1 norm:3 advantageous:1 c0:1 seek:5 covariance:5 decomposition:1 elisseeff:1 reduction:44 contains:1 selecting:1 rkhs:3 outperforms:2 existing:1 comparing:1 exy:1 assigning:1 yet:2 numerical:2 designed:1 plot:2 pursued:1 discovering:2 fewer:1 selected:1 generative... |
3,449 | 4,123 | Repeated Games against Budgeted Adversaries
Manfred K. Warmuth?
Department of Computer Science
UC Santa Cruz
manfred@cse.ucsc.edu
Jacob Abernethy?
Division of Computer Science
UC Berkeley
jake@cs.berkeley.edu
Abstract
We study repeated zero-sum games against an adversary on a budget. Given that
an adversary has some... | 4123 |@word trial:1 private:1 version:5 briefly:1 achievable:1 open:3 termination:1 willing:1 jacob:1 incurs:2 shot:1 reduction:3 prefix:1 past:1 err:1 current:4 surprising:2 si:8 yet:2 intriguing:1 must:7 cruz:1 subsequent:1 half:2 intelligence:1 guess:2 warmuth:3 beginning:1 ith:3 record:1 manfred:2 completeness:1 pr... |
3,450 | 4,124 | Practical Large-Scale Optimization
for Max-Norm Regularization
Jason Lee
Institute of Computational and Mathematical Engineering
Stanford University
email: jl115@yahoo.com
Ruslan Salakhutdinov
Brain and Cognitive Sciences and CSAIL
Massachusetts Institute of Technology
email: rsalakhu@mit.edu
Benjamin Recht
Departmen... | 4124 |@word trial:5 version:1 polynomial:1 norm:68 nd:1 heuristically:1 seek:1 linearized:1 r:1 decomposition:3 prominence:1 pick:1 dramatic:1 initial:1 substitution:1 contains:2 score:1 efficacy:1 series:1 tabulate:1 pub:1 outperforms:3 current:2 com:1 ka:1 si:2 belmont:1 chicago:2 numerical:2 wanted:1 designed:1 upda... |
3,451 | 4,125 | A Dirty Model for Multi-task Learning
Ali Jalali
University of Texas at Austin
alij@mail.utexas.edu
Pradeep Ravikumar
University of Texas at Asutin
pradeepr@cs.utexas.edu
Sujay Sanghavi
University of Texas at Austin
sanghavi@mail.utexas.edu
Chao Ruan
University of Texas at Austin
ruan@cs.utexas.edu
Abstract
We con... | 4125 |@word multitask:2 repository:1 norm:11 stronger:1 nd:1 turlach:1 cleanly:1 km:2 simulation:2 r:2 covariance:3 boundedness:2 liu:1 series:2 disparity:1 denoting:1 interestingly:1 outperforms:5 ksk1:1 recovered:1 yet:1 written:1 realistic:2 additive:1 plot:1 caveat:2 location:1 allerton:2 zhang:1 five:2 c2:6 specia... |
3,452 | 4,126 | A novel family of non-parametric cumulative based
divergences for point processes
Sohan Seth
University of Florida
Il ?Memming? Park
University of Texas at Austin
Mulugeta Semework
SUNY Downstate Medical Center
Austin J. Brockmeier
University of Florida
John Choi, Joseph T. Francis
SUNY Downstate Medical Center & ... | 4126 |@word trial:4 version:1 norm:1 johansson:1 smirnov:4 seek:1 bn:1 contains:1 selecting:2 mainen:1 outperforms:1 abundantly:1 current:2 com:1 vere:1 john:1 fn:5 periodically:1 partition:4 plasticity:3 shape:1 enables:1 reproducible:1 stationary:2 selected:2 prohibitive:1 nq:3 equi:4 complication:1 location:3 mathem... |
3,453 | 4,127 | Learning to localise sounds with spiking neural
networks
Romain Brette
D?epartment d?Etudes Cognitive
Ecole Normale Sup?erieure
29 Rue d?Ulm
Paris 75005, France
romain.brette@ens.fr
Dan F. M. Goodman
D?epartment d?Etudes Cognitive
Ecole Normale Sup?erieure
29 Rue d?Ulm
Paris 75005, France
dan.goodman@ens.fr
Abstract
... | 4127 |@word version:2 compression:1 proportion:1 duda:1 simulation:3 azimuthal:1 pressure:1 mammal:3 solid:1 liu:2 disparity:1 ecole:2 ours:1 interestingly:1 colburn:4 current:1 recovered:1 surprising:1 marquardt:1 activation:3 assigning:2 olive:3 realistic:1 subsequent:1 informative:1 plasticity:3 shape:1 asymptote:1 ... |
3,454 | 4,128 | Predictive Subspace Learning for Multi-view Data:
a Large Margin Approach
Ning Chen??
Jun Zhu?
Eric P. Xing?
?
chenn07@mails.tsinghua.edu.cn, {ningchen,junzhu,epxing}@cs.cmu.edu
?
Dept. of CS & T, TNList Lab, State Key Lab of ITS, Tsinghua University, Beijing 100084 China
?
School of Computer Science, Carnegie Mellon U... | 4128 |@word middle:1 version:3 efh:4 dwh:19 bn:1 contrastive:6 q1:15 mammal:1 tr:1 shot:2 tnlist:1 reduction:1 moment:1 liu:1 contains:2 score:5 africa:2 existing:3 outperforms:3 z2:1 surprising:1 luo:1 csc:1 partition:2 additive:1 chicago:1 designed:1 plot:1 update:3 discovering:10 selected:3 accordingly:2 mccallum:1 ... |
3,455 | 4,129 | A Bayesian Approach to Concept Drift
Stephen H. Bach Marcus A. Maloof
Department of Computer Science
Georgetown University
Washington, DC 20007, USA
{bach, maloof}@cs.georgetown.edu
Abstract
To cope with concept drift, we placed a probability distribution over the location
of the most-recent drift point. We used Baye... | 4129 |@word trial:3 version:1 seems:1 simulation:2 tried:1 phy:1 liu:2 series:3 contains:1 ours:1 past:1 outperforms:1 existing:1 comparing:1 must:3 readily:1 additive:1 partition:2 shape:2 remove:2 update:5 resampling:1 alone:1 generative:1 selected:2 intelligence:1 parameterization:6 detecting:1 parameterizations:6 c... |
3,456 | 413 | An Analog VLSI Chip for Finding Edges
from Zero-crossings
Wyeth Bair
Christof Koch
Computation and Neural Systems Program
Caltech 216-76
Pasadena, CA 91125
Abstract
We have designed and tested a one-dimensional 64 pixel, analog CMOS
VLSI chip which localizes intensity edges in real-time. This device exploits
on-chip ... | 413 |@word aircraft:2 soc:1 implemented:5 version:3 indicate:3 vgi:1 stronger:1 move:1 laboratory:1 filter:13 pulse:1 simulation:4 exclusive:1 settle:1 implementing:1 argued:1 zerocrossings:1 adjusted:1 extension:1 vg2:1 dissipation:1 current:8 length:3 koch:4 image:9 ranging:1 follower:2 john:1 lm:1 circuitry:7 witch:... |
3,457 | 4,130 | Implicit encoding of prior probabilities
in optimal neural populations
Deep Ganguli and Eero P. Simoncelli
Howard Hughes Medical Institute, and
Center for Neural Science
New York University
New York, NY 10003
{dganguli,eero}@cns.nyu.edu
Optimal coding provides a guiding principle for understanding the representation
... | 4130 |@word h:1 cox:1 compression:1 achievable:2 d2:4 seek:1 r:3 contains:1 tuned:5 comparing:1 written:2 must:2 subsequent:2 informative:1 shape:7 opin:1 remove:1 designed:1 plot:1 discrimination:16 cue:1 parameterization:1 cavanaugh:2 parametrization:1 oblique:2 short:2 provides:6 location:1 sigmoidal:1 zhang:1 direc... |
3,458 | 4,131 | Efficient algorithms for learning kernels from
multiple similarity matrices with general convex loss
functions
Vikram Tankasali
Dept. of Computer Science & Automation,
Indian Institute of Science, Bangalore.
vikram@csa.iisc.ernet.in
Achintya Kundu
Dept. of Computer Science & Automation,
Indian Institute of Science, Ba... | 4131 |@word erate:1 version:2 faculty:2 briefly:1 norm:3 suitably:1 open:2 km:32 decomposition:11 idl:1 mention:1 tr:9 efficacy:1 score:5 tuned:1 bhattacharyya:3 past:1 existing:5 optim:1 si:11 engg:1 analytic:1 christian:1 v:1 plane:1 smith:3 provides:3 org:1 zhang:3 dn:1 viable:1 blast:3 pairwise:1 solver:8 consideri... |
3,459 | 4,132 | The Maximal Causes of Natural Scenes are Edge Filters
Gervasio Puertas?
Frankfurt Institute for Advanced Studies
Goethe-University Frankfurt, Germany
puertas@fias.uni-frankfurt.de
J?org Bornschein?
Frankfurt Institute for Advanced Studies
Goethe-University Frankfurt, Germany
bornschein@fias.uni-frankfurt.de
?
J?org L... | 4132 |@word neurophysiology:2 version:2 stronger:1 nd:1 ucke:4 hyv:2 seek:1 decomposition:1 arti:9 mammal:2 eld:19 initial:3 necessity:1 contains:4 selecting:2 denoting:2 nally:2 recovered:8 si:1 assigning:1 realistic:1 numerical:3 distant:1 csc:1 shape:14 enables:1 plot:5 update:13 generative:15 selected:1 accordingly... |
3,460 | 4,133 | Learning Convolutional Feature Hierarchies for
Visual Recognition
Koray Kavukcuoglu1 , Pierre Sermanet1 , Y-Lan Boureau2,1 ,
Karol Gregor1 , Micha?el Mathieu1 , Yann LeCun1
1
Courant Institute of Mathematical Sciences, New York University
2
INRIA - Willow project-team?
{koray,sermanet,ylan,kgregor,yann}@cs.nyu.edu, mm... | 4133 |@word cox:1 middle:2 version:1 dalal:1 advantageous:2 triggs:1 crucially:1 imn:1 contrastive:1 incurs:1 offending:2 initial:1 series:3 contains:3 score:2 ecole:1 document:1 deconvolutional:1 existing:1 comparing:4 marquardt:1 written:1 shape:1 enables:1 update:5 v:3 greedy:1 selected:1 generative:2 item:1 steepes... |
3,461 | 4,134 | Sidestepping Intractable Inference
with Structured Ensemble Cascades
David Weiss?
Benjamin Sapp?
Ben Taskar
Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104, USA
{djweiss,bensapp,taskar}@cis.upenn.edu
Abstract
For many structured prediction problems, complex models often require adopt... | 4134 |@word eliminating:3 underperform:1 decomposition:7 accounting:1 textonboost:1 reduction:1 configuration:1 contains:1 score:16 jimenez:1 interestingly:1 outperforms:4 past:1 current:1 discretization:1 yet:1 must:1 update:4 grass:1 aside:1 half:2 prohibitive:3 selected:1 guess:1 generative:1 isard:1 alone:1 beginni... |
3,462 | 4,135 | A Theory of Multiclass Boosting
Indraneel Mukherjee
Robert E. Schapire
Princeton University, Department of Computer Science, Princeton, NJ 08540
{imukherj,schapire}@cs.princeton.edu
Abstract
Boosting combines weak classifiers to form highly accurate predictors. Although
the case of binary classification is well und... | 4135 |@word mild:3 eor:21 version:1 stronger:2 hu:1 simplifying:1 jacob:1 tr:1 solid:1 harder:2 reduction:5 initial:1 necessity:1 efficacy:1 current:3 bmr:3 com:1 beygelzimer:1 si:1 yet:1 written:1 must:2 john:1 designed:2 plot:4 drop:1 greedy:2 intelligence:1 guess:1 warmuth:1 ith:1 manfred:2 provides:2 boosting:55 lo... |
3,463 | 4,136 | Tiled convolutional neural networks
Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang Wei Koh, Andrew Y. Ng
Computer Science Department, Stanford University
{quocle,jngiam,zhenghao,danchia,pangwei,ang}@cs.stanford.edu
Abstract
Convolutional neural networks (CNNs) have been successfully applied to many
tasks s... | 4136 |@word multitask:1 cnn:11 version:1 crucially:1 rgb:2 covariance:2 hsieh:1 decorrelate:1 innermost:1 liblinear:1 initial:1 contains:3 score:2 selecting:1 daniel:1 tuned:2 document:2 interestingly:1 outperforms:1 existing:1 activation:3 informative:1 cheap:1 depict:1 update:1 alone:1 greedy:2 fewer:2 leaf:1 selecte... |
3,464 | 4,137 | A Family of Penalty Functions for Structured
Sparsity
Charles A. Micchelli?
Department of Mathematics
City University of Hong Kong
83 Tat Chee Avenue, Kowloon Tong
Hong Kong
charles micchelli@hotmail.com
Jean M. Morales
Department of Computer Science
University College London
Gower Street, London WC1E
England, UK
j.m... | 4137 |@word kong:2 version:3 inversion:1 polynomial:3 norm:16 nd:1 closure:1 tat:1 simulation:4 jacob:2 configuration:1 series:2 current:3 com:2 incidence:1 yet:1 must:2 readily:2 numerical:4 partition:24 j1:1 shape:3 v:3 stationary:1 half:1 nq:3 beginning:1 fa9550:1 provides:5 iterates:1 location:1 zhang:1 constructed... |
3,465 | 4,138 | Generating more realistic images using gated MRF?s
Marc?Aurelio Ranzato
Volodymyr Mnih
Geoffrey E. Hinton
Department of Computer Science
University of Toronto
{ranzato,vmnih,hinton}@cs.toronto.edu
Abstract
Probabilistic models of natural images are usually evaluated by measuring performance on rather indirect tasks, ... | 4138 |@word version:1 eliminating:1 norm:1 seems:1 replicate:1 covariance:28 accounting:1 contrastive:5 concise:1 inpainting:2 versatile:2 initial:1 inefficiency:1 tuned:1 document:1 outperforms:1 existing:2 reaction:1 current:3 comparing:2 com:1 surprising:1 lang:1 conjunctive:1 john:1 realistic:3 distant:1 bmcv:1 des... |
3,466 | 4,139 | Learning the context of a category
Daniel J. Navarro
School of Psychology
University of Adelaide
Adelaide, SA 5005, Australia
daniel.navarro@adelaide.edu.au
Abstract
This paper outlines a hierarchical Bayesian model for human category learning
that learns both the organization of objects into categories, and the cont... | 4139 |@word version:2 nd:1 essay:1 schomaker:1 seek:1 covariance:5 thereby:1 accommodate:1 initial:1 configuration:1 selecting:1 daniel:2 ecole:1 contextual:2 comparing:1 yet:1 must:1 partition:10 informative:3 shape:1 plot:1 cue:1 item:21 inspection:2 xk:3 ith:7 cursory:1 short:2 core:1 provides:2 readability:1 succes... |
3,467 | 414 | Real-time autonomous robot navigation using
VLSI neural networks
Lionel Tarassenko Michael Brownlow Gillian Marshall?
Department of Engineering Science
Oxford University, Oxford, OXl 3PJ, UK
Jon Tombs
Alan Murray
Department of Electrical Engineering
Edinburgh University, Edinburgh, EH9 3JL, UK
Abstract
We describe ... | 414 |@word middle:2 pillar:2 open:3 pulse:5 simulation:1 pick:1 solid:1 electronics:2 initial:1 selecting:1 reynolds:1 current:3 yet:1 realistic:1 motor:5 drop:1 designed:2 device:2 short:1 node:12 direct:1 resistive:10 notably:1 planning:7 discretized:2 considering:1 becomes:1 circuit:4 developed:1 whilst:1 every:5 ro... |
3,468 | 4,140 | (RF)2 ? Random Forest Random Field
Nadia Payet and Sinisa Todorovic
School of Electrical Engineering and Computer Science
Oregon State University
payetn@onid.orst.edu, sinisa@eecs.oregonstate.edu
Abstract
We combine random forest (RF) and conditional random field (CRF) into a new
computational framework, called rando... | 4140 |@word triggs:1 yja:1 textonboost:1 bai:1 configuration:1 contains:1 ours:1 outperforms:2 existing:2 contextual:3 finest:3 wiewiora:1 partition:3 informative:1 shape:3 enables:1 grass:1 cue:3 leaf:30 pursued:1 intelligence:1 selected:1 mccallum:1 record:2 colored:1 provides:3 iterates:2 node:28 location:3 complete... |
3,469 | 4,141 | Gaussian Process Preference Elicitation
Edwin V. Bonilla, Shengbo Guo, Scott Sanner
NICTA & ANU, Locked Bag 8001, Canberra ACT 2601, Australia
{edwin.bonilla, shengbo.guo, scott.sanner}@nicta.com.au
Abstract
Bayesian approaches to preference elicitation (PE) are particularly attractive due
to their ability to explicit... | 4141 |@word exploitation:1 version:1 manageable:1 itrue:2 nd:2 twelfth:1 covariance:11 carry:1 initial:3 contains:1 ours:2 outperforms:3 past:1 freitas:1 current:2 com:1 surprising:1 chu:2 ronald:1 refines:1 additive:1 informative:2 remove:1 treating:1 update:3 intelligence:7 selected:1 item:42 parameterization:1 provi... |
3,470 | 4,142 | Trading off Mistakes and Don?t-Know Predictions
Amin Sayedi?
Tepper School of Business
CMU
Pittsburgh, PA 15213
ssayedir@cmu.edu
Morteza Zadimoghaddam?
CSAIL
MIT
Cambridge, MA 02139
morteza@mit.edu
Avrim Blum?
Department of Computer Science
CMU
Pittsburgh, PA 15213
avrim@cs.cmu.edu
Abstract
We discuss an online lea... | 4142 |@word version:5 polynomial:15 citeseer:1 diuk:2 mention:2 recursively:1 reduction:1 contains:2 current:7 yet:2 must:7 additive:1 happen:3 partition:1 remove:4 drop:1 update:1 intelligence:2 fewer:1 selected:1 beginning:3 core:31 incorrect:1 consists:1 combine:1 lov:1 expected:1 indeed:1 roughly:1 examine:1 decrea... |
3,471 | 4,143 | Pose-Sensitive Embedding
by Nonlinear NCA Regression
Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro and Christoph Bregler
Courant Institute of Mathematics, New York University
New York, USA 10003
gwtaylor,fergus,spiro,bregler@cs.nyu.edu
Abstract
This paper tackles the complex problem of visually matching pe... | 4143 |@word cox:2 dalal:1 everingham:1 triggs:2 strong:1 seek:1 contrastive:3 dramatic:1 tr:1 harder:1 ld:3 reduction:3 initial:1 configuration:2 contains:4 jimenez:1 ours:1 document:1 poser:1 existing:1 current:1 ncar:27 protection:1 nowlan:1 goldberger:1 yet:1 must:1 realistic:3 shape:4 designed:2 gist:17 update:1 pl... |
3,472 | 4,144 | Lower Bounds on Rate of Convergence of Cutting
Plane Methods
Xinhua Zhang
Dept. of Computing Science
University of Alberta
xinhua2@ualberta.ca
Ankan Saha
Dept. of Computer Science
University of Chicago
ankans@cs.uchicago.edu
S.V. N. Vishwanathan
Dept. of Statistics and
Dept. of Computer Science
Purdue University
vish... | 4144 |@word mild:1 kgk:1 msr:1 version:3 briefly:1 norm:4 nd:9 open:7 d2:1 bn:1 q1:3 pick:1 tr:1 outlook:2 harder:2 initial:1 contains:2 score:10 series:2 past:1 must:1 written:1 john:1 slb:10 chicago:1 kdd:1 hofmann:1 aside:1 alone:1 devising:2 plane:7 xk:6 steepest:1 core:1 iterates:4 math:1 org:1 zhang:3 mathematica... |
3,473 | 4,145 | Predicting Execution Time of Computer Programs
Using Sparse Polynomial Regression
Ling Huang
Intel Labs Berkeley
ling.huang@intel.com
Byung-Gon Chun
Intel Labs Berkeley
byung-gon.chun@intel.com
Jinzhu Jia
UC Berkeley
jzjia@stat.berkeley.edu
Petros Maniatis
Intel Labs Berkeley
petros.maniatis@intel.com
Bin Yu
UC Berk... | 4145 |@word collinearity:1 polynomial:37 norm:1 turlach:1 retraining:1 tedious:1 mehta:1 seek:2 simulation:1 r:3 paid:1 pick:1 solid:1 reduction:1 necessity:1 configuration:3 cellphone:1 contains:2 liu:2 series:1 past:1 existing:5 current:1 com:4 nt:1 must:3 visible:1 additive:8 numerical:1 shakespeare:1 cheap:1 drop:2... |
3,474 | 4,146 | Reward Design via Online Gradient Ascent
Jonathan Sorg
Computer Science and Eng.
University of Michigan
jdsorg@umich.edu
Satinder Singh
Computer Science and Eng.
University of Michigan
baveja@umich.edu
Richard L. Lewis
Department of Psychology
University of Michigan
rickl@umich.edu
Abstract
Recent work has demonstr... | 4146 |@word h:1 trial:4 version:2 polynomial:2 stronger:1 nd:1 crucially:2 eng:2 thereby:1 solid:5 boundedness:1 initial:4 contains:1 score:2 genetic:3 outperforms:3 existing:2 current:7 nuttapong:1 assigning:1 must:3 readily:1 sorg:4 plot:3 update:2 stationary:1 greedy:1 selected:1 intelligence:3 parameterization:24 h... |
3,475 | 4,147 | Kernel Descriptors for Visual Recognition
Liefeng Bo
University of Washington
Seattle WA 98195, USA
Xiaofeng Ren
Intel Labs Seattle
Seattle WA 98105, USA
Dieter Fox
University of Washington & Intel Labs Seattle
Seattle WA 98195 & 98105, USA
Abstract
The design of low-level image features is critical for computer vis... | 4147 |@word kondor:1 dalal:1 norm:1 triggs:1 open:1 km:2 confirms:1 tried:1 rgb:2 decomposition:3 covariance:2 brightness:1 shot:1 shechtman:1 reduction:1 contains:1 tuned:2 denoting:1 past:1 outperforms:2 existing:1 discretization:1 surprising:2 si:1 written:1 gpu:1 subsequent:1 visible:1 shape:21 designed:2 drop:2 gi... |
3,476 | 4,148 | Joint Cascade Optimization Using a Product of
Boosted Classifiers
Franc?ois Fleuret
Idiap Research Institute
Martigny, Switzerland
francois.fleuret@idiap.ch
Leonidas Lefakis
Idiap Research Institute
Martigny, Switzerland
leonidas.lefakis@idiap.ch
Abstract
The standard strategy for efficient object detection consists ... | 4148 |@word version:7 dalal:1 polynomial:1 triggs:1 reduction:2 initial:2 cyclic:2 contains:1 score:1 necessity:1 bootstrapped:1 ours:1 outperforms:2 jinbo:1 must:3 numerical:1 visible:1 shape:1 hofmann:1 update:1 ashutosh:1 aside:1 alone:1 greedy:1 v:2 intelligence:2 core:1 detecting:1 boosting:14 coarse:2 location:3 ... |
3,477 | 4,149 | Large Margin Learning of Upstream
Scene Understanding Models
?
Jun Zhu?
Li-Jia Li?
Fei-Fei Li?
Eric P. Xing?
?
{junzhu,epxing}@cs.cmu.edu
{lijiali,feifeili}@cs.stanford.edu
?
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
?
Department of Computer Science, Stanford University, Stanford, C... | 4149 |@word version:1 norm:2 covariance:1 brightness:1 dramatic:1 thereby:1 reduction:1 liu:1 contains:2 score:1 interestingly:1 outperforms:3 existing:3 current:1 comparing:1 written:1 partition:2 shape:1 designed:1 gist:10 update:1 concert:1 alone:1 generative:1 discovering:2 selected:1 plane:2 lr:4 blei:4 iterates:1... |
3,478 | 415 | Oriented Non-Radial Basis Functions for Image
Coding and Analysis
Avijit Saha 1
Jim Christian
D. S. Tang
Microelectronics and Computer Technology Corporation
3500 West Balcones Center Drive
Austin, TX 78759
Chuan-Lin Wu
Department of Electrical and Computer Engineering
University of Texas at Austin,
Austin, TX 7871... | 415 |@word illustrating:1 polynomial:4 advantageous:1 suitably:1 grey:1 d2:1 fonn:5 tr:1 reduction:2 initial:1 series:4 tuned:2 kurt:1 bitmap:4 elliptical:1 dx:1 readily:3 john:2 subsequent:1 j1:1 shape:3 christian:4 girosi:1 designed:2 interpretable:1 update:1 plot:2 alone:1 tenn:1 ith:1 coarse:1 node:1 location:2 alo... |
3,479 | 4,150 | Probabilistic Multi-Task Feature Selection
1
Yu Zhang1 , Dit-Yan Yeung1 , Qian Xu2
Department of Computer Science and Engineering, 2 Bioengineering Program
Hong Kong University of Science and Technology
{zhangyu,dyyeung}@cse.ust.hk, fleurxq@ust.hk
Abstract
Recently, some variants of the ?1 norm, particularly matrix ... | 4150 |@word multitask:1 kong:2 determinant:1 version:3 trial:3 norm:34 seems:1 nd:3 turlach:1 tamayo:1 seek:1 covariance:3 delgado:1 liu:3 contains:3 series:1 selecting:1 document:1 outperforms:2 existing:4 ust:2 written:1 numerical:1 noninformative:3 update:2 mtfl:3 fund:1 intelligence:1 asu:1 selected:1 huo:1 renshaw... |
3,480 | 4,151 | Construction of Dependent Dirichlet Processes
based on Poisson Processes
Dahua Lin
CSAIL, MIT
dhlin@mit.edu
Eric Grimson
CSAIL, MIT
welg@csail.mit.edu
John Fisher
CSAIL, MIT
fisher@csail.mit.edu
Abstract
We present a novel method for constructing dependent Dirichlet processes. The
approach exploits the intrinsic rel... | 4151 |@word trial:3 version:2 briefly:1 open:1 simulation:5 covariance:1 series:1 genetic:1 document:2 ours:1 outperforms:1 existing:3 com:1 comparing:1 yet:1 parsing:1 john:1 remove:1 update:9 website:1 parameterization:1 accordingly:1 record:1 blei:2 provides:3 iterates:1 node:1 location:10 math:1 welg:1 along:1 cons... |
3,481 | 4,152 | Joint Analysis of Time-Evolving Binary Matrices
and Associated Documents
1
Eric Wang, 1 Dehong Liu, 1 Jorge Silva, 2 David Dunson and 1 Lawrence Carin
1
Electrical and Computer Engineering Department, Duke University
2
Statistics Department, Duke University
{eric.wang,dehong.liu,jg.silva,lawrence.carin}@duke.edu
dunso... | 4152 |@word version:1 seems:2 justice:1 closure:1 heretofore:2 gradual:1 yea:1 liu:2 series:1 loc:1 united:4 contains:1 denoting:1 document:52 existing:1 com:4 stemmed:1 must:1 readily:3 import:1 analytic:1 motor:1 plot:4 sponsored:1 update:1 depict:1 v:4 alone:4 generative:3 intelligence:1 website:3 ith:2 record:2 col... |
3,482 | 4,153 | Cross Species Expression Analysis using a Dirichlet
Process Mixture Model with Latent Matchings
Ziv Bar-Joseph
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA, USA
zivbj@cs.cmu.edu
Hai-Son Le
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA, USA
hple@cs.cmu.edu
Abstract
Rec... | 4153 |@word briefly:1 covariance:5 p0:2 series:1 score:3 contains:1 genetic:1 interestingly:1 past:1 lichtenberg:1 blank:1 comparing:2 activation:1 physiol:1 concatenate:1 update:7 zik:2 alone:1 selected:3 core:5 provides:1 detecting:1 org:1 five:2 phylogenetic:1 unbounded:1 constructed:1 direct:1 beta:2 c2:1 symposium... |
3,483 | 4,154 | Discriminative Clustering by Regularized
Information Maximization
Ryan Gomes
gomes@vision.caltech.edu
Andreas Krause
krausea@caltech.edu
Pietro Perona
perona@vision.caltech.edu
California Institute of Technology
Pasadena, CA 91106
Abstract
Is there a principled way to learn a probabilistic discriminative classifier ... | 4154 |@word proportion:4 norm:4 logit:2 hippocampus:1 reused:1 unpopulated:4 additively:1 tkacik:1 initial:2 configuration:2 contains:1 fragment:1 efficacy:1 liu:1 rkhs:1 ours:2 outperforms:6 existing:4 comparing:2 tackling:1 dx:1 must:1 john:4 concatenate:1 partition:2 plot:1 discrimination:1 alone:1 generative:3 inst... |
3,484 | 4,155 | A Novel Kernel for Learning a Neuron Model from
Spike Train Data
Nicholas Fisher, Arunava Banerjee
Department of Computer and Information Science and Engineering
University of Florida
Gainesville, FL 32611
{nfisher,arunava}@cise.ufl.edu
Abstract
From a functional viewpoint, a spiking neuron is a device that transform... | 4155 |@word neurophysiology:1 version:1 rising:1 additively:3 gainesville:1 solid:1 versatile:2 configuration:14 contains:1 efficacy:1 disparity:1 past:8 current:1 assigning:1 must:5 kft:2 written:1 additive:3 hyperpolarizing:1 n0:1 alone:1 device:3 nervous:1 nq:5 reciprocal:1 core:1 readability:1 mathematical:1 along:... |
3,485 | 4,156 | Learning Bounds for Importance Weighting
Corinna Cortes
Google Research
New York, NY 10011
Yishay Mansour
Tel-Aviv University
Tel-Aviv 69978, Israel
Mehryar Mohri
Courant Institute and Google
New York, NY 10012
corinna@google.com
mansour@tau.ac.il
mohri@cims.nyu.edu
Abstract
This paper presents an analysis of im... | 4156 |@word eor:1 version:1 seems:2 nd:1 grey:1 d2:17 moment:15 liu:1 series:3 contains:1 rightmost:1 past:1 com:1 beygelzimer:1 si:1 dx:2 must:1 john:1 informative:1 plot:1 v:1 intelligence:1 selected:3 xk:2 provides:2 boosting:2 math:1 readability:1 hyperplanes:2 simpler:1 quantit:1 unbounded:14 direct:1 symposium:1 ... |
3,486 | 4,157 | Fast detection of multiple change-points shared by
many signals using group LARS
Jean-Philippe Vert and Kevin Bleakley
Mines ParisTech CBIO, Institut Curie, INSERM U900
{firstname.lastname}@mines-paristech.fr
Abstract
We present a fast algorithm for the detection of multiple change-points when each
is frequently shar... | 4157 |@word trial:4 polynomial:1 norm:6 km:1 simulation:1 series:3 selecting:1 genetic:1 denoting:1 existing:3 current:1 must:4 subsequent:1 confirming:1 zaid:1 update:1 prohibitive:1 selected:8 rudin:1 sudden:1 hypersphere:3 detecting:4 provides:1 math:1 location:9 successive:3 zhang:1 height:1 mathematical:1 along:4 ... |
3,487 | 4,158 | Beyond Actions: Discriminative Models for
Contextual Group Activities
Tian Lan
School of Computing Science
Simon Fraser University
tla58@sfu.ca
Yang Wang
Department of Computer Science
University of Illinois at Urbana-Champaign
yangwang@uiuc.edu
Weilong Yang
School of Computing Science
Simon Fraser University
wya16@... | 4158 |@word dalal:1 triggs:1 solid:1 shechtman:1 contains:1 score:2 selecting:1 ours:2 outperforms:3 blank:1 contextual:10 written:2 wiewiora:1 shape:1 hofmann:1 remove:1 alone:2 cue:1 fewer:2 intelligence:2 plane:1 provides:2 node:5 five:4 along:1 zkj:1 incorrect:1 introduce:3 pairwise:2 acquired:1 x0:10 behavior:1 ui... |
3,488 | 4,159 | Inductive Regularized Learning of Kernel Functions
Prateek Jain
Microsoft Research Bangalore
Bangalore, India
prajain@microsoft.com
Brian Kulis
UC Berkeley EECS and ICSI
Berkeley, CA, USA
kulis@eecs.berkeley.edu
Inderjit Dhillon
UT Austin Dept. of Computer Sciences
Austin, TX, USA
inderjit@cs.utexas.edu
Abstract
In... | 4159 |@word kulis:6 briefly:1 norm:10 decomposition:2 elisseeff:1 tr:16 accommodate:1 reduction:25 initial:2 denoting:1 ours:1 existing:6 com:1 jaz:1 goldberger:1 written:1 readily:1 john:1 blur:1 enables:1 update:3 v:2 half:1 fewer:1 selected:2 parameterization:1 warmuth:1 lr:18 colored:2 provides:4 simpler:2 zhang:1 ... |
3,489 | 416 | ALCOVE: A Connectionist Model of
Human Category Learning
John K. Kruschke
Department of Psychology and Cognitive Science Program
Indiana University, Bloomington IN 47405-4201 USA
e-mail: kruschke@ucs.indiana.edu
Abstract
ALCOVE is a connectionist model of human category learning that fits a
broad spectrum of human le... | 416 |@word trial:2 illustrating:1 version:1 open:1 series:1 past:3 activation:6 scatter:1 yet:1 john:1 alone:2 fewer:1 dissertation:2 node:26 simpler:2 become:2 fitting:2 behavioral:1 behavior:2 themselves:1 multi:4 brain:1 decreasing:1 increasing:1 psychometrika:1 discover:1 moreover:1 panel:3 psych:4 indiana:4 berkel... |
3,490 | 4,160 | Bootstrapping Apprenticeship Learning
Abdeslam Boularias
Department of Empirical Inference
Max-Planck Institute for Biological Cybernetics
72076 T?ubingen, Germany
abdeslam.boularias@tuebingen.mpg.de
Brahim Chaib-Draa
Department of Computer Science
Laval University
Quebec G1V 0A6, Canada
chaib@damas.ift.ulaval.ca
Ab... | 4160 |@word mild:1 trial:1 briefly:1 proportion:1 pieter:1 homomorphism:1 initial:4 bootstrapped:8 outperforms:1 bradley:1 current:1 yet:1 written:1 designed:1 intelligence:2 selected:1 amir:2 provides:2 boosting:1 location:1 simpler:1 consists:4 inside:1 introduce:1 apprenticeship:20 indeed:1 expected:5 behavior:2 mpg... |
3,491 | 4,161 | Probabilistic Deterministic Infinite Automata
David Pfau
Nicholas Bartlett
Frank Wood
Columbia University, New York, NY 10027, USA
{pfau@neurotheory,{bartlett,fwood}@stat}.columbia.edu
Abstract
We propose a novel Bayesian nonparametric approach to learning with probabilistic deterministic finite automata (PDFA). We de... | 4161 |@word compression:2 bigram:2 norm:1 open:1 citeseer:1 q1:1 carry:1 initial:4 fragment:1 ours:2 outperforms:1 surprising:1 yet:1 must:4 informative:1 dupont:2 wanted:1 remove:1 plot:1 update:3 alone:1 generative:4 discovering:1 selected:1 intelligence:1 ith:3 short:1 core:1 accepting:1 memoizer:3 indefinitely:1 bl... |
3,492 | 4,162 | Decontaminating Human Judgments
by Removing Sequential Dependencies
Michael C. Mozer,? Harold Pashler,? Matthew Wilder,?
Robert V. Lindsey,? Matt C. Jones,? & Michael N. Jones?
?
Dept. of Computer Science, University of Colorado
?
Dept. of Psychology, UCSD
?
Dept. of Psychology, University of Colorado
?
Dept. of Psych... | 4162 |@word trial:36 middle:1 faculty:1 achievable:1 proportion:2 seems:2 compression:8 briefly:1 judgement:1 instruction:1 contrastive:1 paid:2 thereby:1 minus:1 reduction:7 initial:1 configuration:2 series:7 score:1 hereafter:1 rightmost:1 mumma:3 subjective:1 current:8 comparing:2 com:1 surprising:1 must:2 additive:... |
3,493 | 4,163 | Learning Multiple Tasks using Manifold
Regularization
Arvind Agarwal?
Hal Daum?e III?
Department of Computer Science
University of Maryland
College Park, MD 20740
arvinda@cs.umd.edu
hal@umiacs.umd.edu
Samuel Gerber
Scientific Computing and Imaging Institute
University of Utah
Salt Lake City, Utah 84112
sgerber@cs.uta... | 4163 |@word multitask:11 briefly:1 middle:1 norm:1 lenk:1 covariance:1 jacob:1 tr:1 reduction:7 initial:1 liu:1 efficacy:1 score:1 denoting:1 rkhs:7 ours:1 tuned:3 outperforms:1 existing:5 current:2 nt:2 written:2 kdd:1 remove:1 plot:1 v:2 intelligence:1 fewer:3 plane:1 completeness:1 authority:1 simpler:1 zhang:3 cons... |
3,494 | 4,164 | Distributed Dual Averaging in Networks
John C. Duchi1
Alekh Agarwal1
Martin J. Wainwright1,2
Department of Electrical Engineering and Computer Science1 and Department of Statistics2
University of California, Berkeley
Berkeley, CA 94720-1776
{jduchi,alekh,wainwrig}@eecs.berkeley.edu
Abstract
The goal of decentralized o... | 4164 |@word trial:1 version:3 seems:1 norm:5 johansson:4 simulation:5 decomposition:3 commute:1 solid:1 contains:1 past:1 wainwrig:1 current:3 must:1 written:1 john:1 numerical:1 plot:5 update:6 device:1 iterates:2 provides:2 node:32 characterization:1 org:2 mathematical:2 constructed:1 symposium:1 consists:2 doubly:3 ... |
3,495 | 4,165 | MAP Estimation for Graphical Models by
Likelihood Maximization
Shlomo Zilberstein
Department of Computer Science
University of Massachusetts
Amherst, MA
shlomo@cs.umass.edu
Akshat Kumar
Department of Computer Science
University of Massachusetts
Amherst, MA
akshat@cs.umass.edu
Abstract
Computing a maximum a posteriori... | 4165 |@word kohli:1 a8i:1 d2:2 seek:1 simplifying:1 p0:5 dramatic:1 phy:1 configuration:1 series:1 uma:2 outperforms:1 current:2 assigning:1 dechter:1 partition:1 shlomo:2 plot:1 update:5 intelligence:3 selected:1 core:1 fa9550:1 certificate:1 provides:4 node:13 coarse:1 simpler:3 become:1 consists:2 pairwise:4 expecte... |
3,496 | 4,166 | Parametric Bandits:
The Generalized Linear Case
Olivier Capp?e
LTCI
Telecom ParisTech et CNRS
Paris, France
cappe@telecom-paristech.fr
Sarah Filippi
LTCI
Telecom ParisTech et CNRS
Paris, France
filippi@telecom-paristech.fr
Aur?elien Garivier
LTCI
Telecom ParisTech et CNRS
Paris, France
garivier@telecom-paristech.fr
C... | 4166 |@word repository:1 version:1 briefly:1 exploitation:2 seems:1 norm:2 proportion:3 open:1 km:12 d2:1 simulation:2 hu:1 harder:1 moment:1 contains:3 selecting:1 tuned:3 interestingly:2 past:1 outperforms:2 existing:1 current:2 nt:3 numerical:3 plot:1 greedy:9 selected:1 capitalizes:1 xk:7 record:2 transposition:1 b... |
3,497 | 4,167 | The Neural Costs of Optimal Control
Samuel J. Gershman and Robert C. Wilson
Psychology Department and Neuroscience Institute
Princeton University
Princeton, NJ 08540
{sjgershm,rcw2}@princeton.edu
Abstract
Optimal control entails combining probabilities and utilities. However, for most
practical problems, probability ... | 4167 |@word seems:2 seek:2 thereby:1 moment:1 valois:1 series:1 selecting:1 tuned:2 interestingly:1 casas:1 savage:1 comparing:1 jaynes:1 intriguing:1 must:3 fn:3 additive:1 confirming:1 shape:1 update:5 intelligence:2 dover:1 oblique:2 harvesting:1 provides:3 location:3 constructed:1 direct:1 consists:1 behavioral:2 m... |
3,498 | 4,168 | Universal Kernels on Non-Standard Input Spaces
Andreas Christmann
University of Bayreuth
Department of Mathematics
D-95440 Bayreuth
andreas.christmann@uni-bayreuth.de
Ingo Steinwart
University of Stuttgart
Department of Mathematics
D-70569 Stuttgart
ingo.steinwart@mathematik.uni-stuttgart.de
Abstract
During the last ... | 4168 |@word version:2 seems:2 norm:6 nd:1 p0:8 pick:1 mention:1 reduction:2 series:5 contains:2 wj2:2 rkhs:14 interestingly:1 scovel:2 discretization:1 universality:12 dx:6 john:1 analytic:1 n0:7 intelligence:1 fewer:1 smith:1 short:1 colored:3 provides:2 math:2 bijection:1 zhang:1 constructed:1 symposium:1 qualitative... |
3,499 | 4,169 | Phone Recognition with the Mean-Covariance
Restricted Boltzmann Machine
George E. Dahl, Marc?Aurelio Ranzato, Abdel-rahman Mohamed, and Geoffrey Hinton
Department of Computer Science
University of Toronto
{gdahl, ranzato, asamir, hinton}@cs.toronto.edu
Abstract
Straightforward application of Deep Belief Nets (DBNs) to... | 4169 |@word inversion:1 bigram:2 norm:2 confirms:1 covariance:21 pressure:2 dramatic:1 pick:1 tr:1 harder:1 reduction:1 initial:1 configuration:2 inefficiency:2 efficacy:1 substitution:1 seriously:1 activation:6 must:4 visible:19 partition:1 speakerindependent:1 ldc93s1:1 informative:1 utml:1 remove:2 designed:1 plot:1... |
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