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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6,800 | 7,151 | On Fairness and Calibration
Geoff Pleiss?, Manish Raghavan?, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger
Cornell University, Department of Computer Science
{geoff,manish,kleinber}@cs.cornell.edu,
{fw245,kwq4}@cornell.edu
Abstract
The machine learning community has become increasingly concerned with the
potential for... | 7151 |@word repository:3 faculty:1 middle:2 achievable:1 consequential:1 justice:10 prognostic:1 seek:2 zliobaite:1 paid:1 incurs:3 carry:2 reduction:1 venkatasubramanian:1 initial:1 contains:2 disparity:7 score:5 occupational:1 unintended:1 lichman:1 existing:4 comparing:1 com:1 contextual:1 yet:1 must:6 fn:8 kdd:3 re... |
6,801 | 7,152 | Imagination-Augmented Agents
for Deep Reinforcement Learning
S?bastien Racani?re? Th?ophane Weber? David P. Reichert? Lars Buesing
Arthur Guez Danilo Rezende Adria Puigdom?nech Badia Oriol Vinyals
Nicolas Heess Yujia Li Razvan Pascanu
Peter Battaglia
Demis Hassabis David Silver Daan Wierstra
DeepMind
Abstract
We intr... | 7152 |@word katja:1 trial:1 exploitation:1 version:2 seems:1 nd:2 nonsensical:1 reused:2 open:1 instruction:1 pieter:7 simulation:13 seek:2 rgb:2 accounting:1 schoellig:1 thereby:1 tr:1 shot:1 catastrophically:1 initial:3 liu:2 contains:2 score:1 selecting:1 exclusively:1 daniel:1 past:2 existing:1 hasselt:2 current:5 ... |
6,802 | 7,153 | Extracting low-dimensional dynamics from
multiple large-scale neural population recordings
by learning to predict correlations
1
Marcel Nonnenmacher1 , Srinivas C. Turaga2 and Jakob H. Macke1?
research center caesar, an associate of the Max Planck Society, Bonn, Germany
2
HHMI Janelia Research Campus, Ashburn, VA
mar... | 7153 |@word neurophysiology:1 trial:1 private:1 version:2 norm:2 seems:1 busing:1 r:1 simulation:3 lobe:1 covariance:56 decomposition:1 tr:1 briggman:1 moment:5 reduction:10 liu:2 series:2 initial:2 initialisation:2 outperforms:2 current:2 perturbative:1 readily:3 numerical:1 subsequent:1 informative:1 realistic:2 opin... |
6,803 | 7,154 | Unifying PAC and Regret: Uniform PAC Bounds for
Episodic Reinforcement Learning
Tor Lattimore?
tor.lattimore@gmail.com
Christoph Dann
Machine Learning Department
Carnegie-Mellon University
cdann@cdann.net
Emma Brunskill
Computer Science Department
Stanford University
ebrun@cs.stanford.edu
Abstract
Statistical perfo... | 7154 |@word h:1 exploitation:1 version:6 briefly:2 polynomial:5 stronger:5 achievable:1 open:2 p0:5 boundedness:1 reduction:1 initial:1 sah:9 exclusively:1 contains:2 daniel:1 interestingly:1 past:2 existing:6 current:1 com:2 comparing:1 contextual:3 analysed:1 si:2 gmail:1 yet:1 must:2 john:2 ronald:3 designed:1 updat... |
6,804 | 7,155 | Gradients of Generative Models for Improved
Discriminative Analysis of Tandem Mass Spectra
John T. Halloran
Department of Public Health Sciences
University of California, Davis
jthalloran@ucdavis.edu
David M. Rocke
Department of Public Health Sciences
University of California, Davis
dmrocke@ucdavis.edu
Abstract
Tand... | 7155 |@word version:3 middle:1 proportion:1 open:4 heuristically:5 bn:4 accounting:1 eng:3 pavel:1 contains:1 score:39 fragment:13 liquid:1 series:1 denoting:3 prefix:1 past:2 existing:1 outperforms:1 current:3 si:8 yet:1 parsing:1 john:5 readily:1 keich:2 drop:1 plot:2 designed:1 farkas:2 v:1 generative:13 leaf:1 inte... |
6,805 | 7,156 | Asynchronous Parallel Coordinate Minimization
for MAP Inference
Ofer Meshi
Google
meshi@google.com
Alexander G. Schwing
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
aschwing@illinois.edu
Abstract
Finding the maximum a-posteriori (MAP) assignment is a central task for st... | 7156 |@word mild:1 kohli:1 version:2 middle:2 achievable:1 pw:1 norm:2 vldb:1 simulation:1 decomposition:10 hsieh:4 p0:1 pick:2 tr:3 harder:1 configuration:9 liu:8 disparity:6 score:2 ours:3 bhattacharyya:2 miklau:1 past:1 current:3 com:1 yet:2 readily:1 numerical:3 happen:1 realistic:1 cheap:1 drop:1 update:39 v:4 int... |
6,806 | 7,157 | Multiscale Quantization for Fast Similarity Search
Xiang Wu Ruiqi Guo Ananda Theertha Suresh Sanjiv Kumar
Dan Holtmann-Rice David Simcha Felix X. Yu
Google Research, New York
{wuxiang, guorq, theertha, sanjivk, dhr, dsimcha, felixyu}@google.com
Abstract
We propose a multiscale quantization approach for fast similarity... | 7157 |@word version:1 ruiqi:1 compression:1 inversion:1 norm:40 instruction:3 d2:1 vldb:1 covariance:1 sgd:1 incurs:1 harder:1 reduction:7 contains:1 reine:1 existing:3 com:1 babenko:4 anne:1 activation:1 gpu:1 sanjiv:1 partition:22 additive:8 wx:4 kqj:4 enables:2 razenshteyn:1 plot:3 v:1 beginning:1 short:1 footing:1 ... |
6,807 | 7,158 | Diverse and Accurate Image Description Using a
Variational Auto-Encoder with an Additive Gaussian
Encoding Space
Liwei Wang
Alexander G. Schwing
Svetlana Lazebnik
{lwang97, aschwing, slazebni}@illinois.edu
University of Illinois at Urbana-Champaign
Abstract
This paper explores image caption generation using condition... | 7158 |@word cnn:2 version:1 briefly:1 stronger:1 open:5 crucially:1 covariance:1 p0:1 dramatic:1 incurs:1 sgd:2 mention:2 rivera:1 accommodate:1 liu:2 contains:5 score:3 tuned:1 cvae:114 current:2 com:1 guadarrama:1 luo:1 activation:1 attracted:1 readily:1 must:1 neuraltalk2:3 additive:9 partition:1 realistic:2 analyti... |
6,808 | 7,159 | Improved Training of Wasserstein GANs
Ishaan Gulrajani1?, Faruk Ahmed1 , Martin Arjovsky2 , Vincent Dumoulin1 , Aaron Courville1,3
1
Montreal Institute for Learning Algorithms
2
Courant Institute of Mathematical Sciences
3
CIFAR Fellow
igul222@gmail.com
{faruk.ahmed,vincent.dumoulin,aaron.courville}@umontreal.ca
ma4371... | 7159 |@word mild:1 cnn:3 version:2 norm:19 seems:2 stronger:1 villani:1 open:2 hu:1 tried:1 bn:2 bachman:1 jacob:1 pg:22 moment:2 initial:2 liu:1 contains:2 score:15 ours:3 outperforms:3 com:2 activation:1 gmail:1 assigning:1 must:2 enables:1 plot:6 drop:3 update:1 designed:1 generative:21 alec:1 vanishing:3 farther:1 ... |
6,809 | 716 | On the Use of Evidence in Neural Networks
David H. Wolpert
The Santa Fe Institute
1660 Old Pecos Trail
Santa Fe, NM 87501
Abstract
The Bayesian "evidence" approximation has recently been employed to
determine the noise and weight-penalty terms used in back-propagation.
This paper shows that for neural nets it is far e... | 716 |@word private:1 open:1 proportionality:3 recounted:2 thatfor:1 mention:2 tr:1 initial:1 subjective:3 current:1 surprising:2 yet:1 must:16 numerical:2 additive:1 remove:1 v:1 tenn:1 guess:4 accordingly:1 simpler:2 direct:1 become:1 ik:1 incorrect:1 prove:4 fitting:1 combine:1 manner:1 introduce:2 indeed:2 behavior:... |
6,810 | 7,160 | Learning Populations of Parameters
Kevin Tian, Weihao Kong, and Gregory Valiant
Department of Computer Science
Stanford University
Stanford, CA, 94305
(kjtian, whkong, valiant)@stanford.edu
Abstract
Consider the following estimation problem: there are n entities, each with an
unknown parameter pi ? [0, 1], and we obs... | 7160 |@word kong:2 trial:1 faculty:1 version:3 polynomial:9 stronger:1 clts:1 sex:4 q1:1 shot:1 moment:54 score:1 daniel:2 genetic:4 bootstrapped:2 seriously:1 denoting:1 past:1 recovered:12 current:1 nt:1 surprising:1 discretization:1 nicolai:1 dx:2 must:2 danny:1 subsequent:1 plot:2 update:1 v:1 half:1 intelligence:1... |
6,811 | 7,161 | Clustering with Noisy Queries
Arya Mazumdar and Barna Saha
College of Information and Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
{arya,barna}@cs.umass.edu
Abstract
In this paper, we provide a rigorous theoretical study of clustering with noisy
queries. Given a set of n elements, our goal ... | 7161 |@word faculty:1 version:2 polynomial:6 nd:1 c0:5 open:1 hsieh:1 p0:11 eng:1 pick:2 asks:3 reduction:2 contains:7 uma:1 selecting:3 karger:1 neeman:1 document:1 interestingly:2 franklin:1 existing:1 recovered:2 assigning:3 intriguing:1 must:13 john:1 partition:2 j1:3 remove:3 treating:1 plot:1 update:3 v:5 resampl... |
6,812 | 7,162 | Higher-Order Total Variation Classes on Grids:
Minimax Theory and Trend Filtering Methods
Veeranjaneyulu Sadhanala
Carnegie Mellon University
Pittsburgh, PA 15213
vsadhana@cs.cmu.edu
Yu-Xiang Wang
Carnegie Mellon University/Amazon AI
Pittsburgh, PA 15213/Palo Alto, CA 94303
yuxiangw@amazon.com
James Sharpnack
Univer... | 7162 |@word middle:2 version:2 polynomial:5 seek:1 bn:9 invoking:1 series:2 contains:8 interestingly:1 past:1 com:1 incidence:2 discretization:1 comparing:3 dx:4 must:2 written:1 sergei:1 numerical:1 additive:2 j1:1 christian:1 zaid:1 designed:1 aside:1 v:1 intelligence:1 fewer:1 rudin:1 beginning:1 reciprocal:2 smith:... |
6,813 | 7,163 | Training Quantized Nets: A Deeper Understanding
Hao Li1?, Soham De1?, Zheng Xu1 , Christoph Studer2 , Hanan Samet1 , Tom Goldstein1
1
Department of Computer Science, University of Maryland, College Park
2
School of Electrical and Computer Engineering, Cornell University
{haoli,sohamde,xuzh,hjs,tomg}@cs.umd.edu, studer@... | 7163 |@word exploitation:7 version:1 coarseness:1 annapureddy:1 retraining:1 tried:1 jacob:1 sgd:11 solid:1 ld:1 initial:1 liu:1 selecting:2 tuned:1 bc:50 interestingly:1 outperforms:1 bitwise:3 discretization:3 surprising:1 activation:2 assigning:1 dx:9 explorative:1 numerical:1 enables:1 drop:2 plot:5 update:16 progr... |
6,814 | 7,164 | Permutation-based Causal Inference Algorithms
with Interventions
Yuhao Wang
Laboratory for Information and Decision Systems
and Institute for Data, Systems and Society
Massachusetts Institute of Technology
Cambridge, MA 02139
yuhaow@mit.edu
Karren Dai Yang
Institute for Data, Systems and Society
and Broad Institute of ... | 7164 |@word version:1 proportion:5 open:1 simulation:5 mammal:1 thereby:1 solid:2 accommodate:2 initial:1 inefficiency:1 contains:5 score:26 series:1 selecting:1 genetic:1 interestingly:1 recovered:1 com:1 must:1 additive:1 drop:1 plot:4 update:5 designed:1 alone:1 greedy:26 discovering:1 fewer:1 leaf:1 intelligence:1 ... |
6,815 | 7,165 | Time-dependent spatially varying graphical models,
with application to brain fMRI data analysis
Kristjan Greenewald
Department of Statistics
Harvard University
Seyoung Park
Department of Biostatistics
Yale University
Shuheng Zhou
Department of Statistics
University of Michigan
Alexander Giessing
Department of Stati... | 7165 |@word version:2 middle:1 norm:7 stronger:1 confirms:1 simulation:1 pearlson:1 covariance:45 tr:22 initial:1 liu:4 series:4 zij:2 tuned:1 existing:2 ka:7 z2:5 chu:1 must:1 john:1 fn:4 additive:11 realistic:1 confirming:1 motor:1 remove:1 update:1 implying:2 generative:2 selected:1 fewer:1 discovering:1 adal:1 ith:... |
6,816 | 7,166 | Gradient Methods for Submodular Maximization
Hamed Hassani
ESE Department
University of Pennsylvania
Philadelphia, PA
hassani@seas.upenn.edu
Mahdi Soltanolkotabi
EE Department
University of Southern California
Los Angeles, CA
soltanol@usc.edu
Amin Karbasi
ECE Department
Yale University
New Haven, CT
amin.karbasi@yale... | 7166 |@word version:9 polynomial:1 norm:9 nd:1 pick:2 reduction:3 initial:5 selecting:1 document:2 interestingly:1 current:1 reminiscent:1 attracted:1 must:1 kdd:4 update:9 stationary:16 greedy:21 selected:1 intelligence:1 item:1 volkan:1 provides:4 iterates:1 location:5 preference:1 mathematical:3 direct:1 symposium:2... |
6,817 | 7,167 | Smooth Primal-Dual Coordinate Descent Algorithms
for Nonsmooth Convex Optimization
Ahmet Alacaoglu1
1
Quoc Tran-Dinh2
Olivier Fercoq3
Volkan Cevher1
Laboratory for Information and Inference Systems (LIONS), EPFL, Lausanne, Switzerland
{ahmet.alacaoglu, volkan.cevher}@epfl.ch
2
Department of Statistics and Operation... | 7167 |@word repository:1 mri:2 version:1 briefly:1 norm:3 c0:1 hu:3 km:4 simulation:1 q1:1 boundedness:1 contains:1 lichman:1 tist:1 ktv:1 tuned:1 document:2 existing:1 ka:5 com:1 optim:1 rpi:2 dx:1 written:1 numerical:3 plot:9 update:15 prohibitive:1 instantiate:2 kyk:1 xk:24 volkan:2 provides:1 math:2 zhang:1 mathema... |
6,818 | 7,168 | The Importance of Communities for
Learning to Influence
Eric Balkanski
Harvard University
ericbalkanski@g.harvard.edu
Nicole Immorlica
Microsoft Research
nicimm@microsoft.com
Yaron Singer
Harvard University
yaron@seas.harvard.edu
Abstract
We consider the canonical problem of influence maximization in social networks... | 7168 |@word mild:1 private:1 seems:1 nd:2 hu:1 simplifying:1 pick:8 lakshmanan:1 liu:1 contains:2 selecting:2 outperforms:2 com:1 cad:2 manuel:4 si:14 luis:1 john:1 additive:1 partition:1 kdd:5 christian:1 remove:3 drop:2 plot:1 seeding:2 v:2 generative:3 selected:1 greedy:2 ith:3 parkes:1 math:1 node:105 unbounded:1 c... |
6,819 | 7,169 | Multiplicative Weights Update with Constant
Step-Size in Congestion Games: Convergence, Limit
Cycles and Chaos
Gerasimos Palaiopanos?
SUTD
Singapore
gerasimosath@yahoo.com
Ioannis Panageas?
MIT
Cambridge, MA 02139
ioannis@csail.mit.edu
Georgios Piliouras?
SUTD
Singapore
georgios@sutd.edu.sg
Abstract
The Multiplicat... | 7169 |@word version:6 polynomial:6 advantageous:1 suitably:1 open:4 termination:1 mehta:2 hu:1 carry:3 reduction:1 initial:14 series:2 ce2:3 ours:1 interestingly:1 current:2 com:1 luo:1 si:22 must:1 realistic:1 partition:1 numerical:1 plot:4 ligett:2 update:18 congestion:44 intelligence:1 discovering:1 coarse:2 charact... |
6,820 | 717 | Amplifying and Linearizing Apical
Synaptic Inputs
to Cortical Pyramidal Cells.
Ojvind Bernander, Christof Koch . .
Computation and Neural Systems Program,
California Institute of Technology, 139-74
Pasadena, Ca 91125, USA.
Rodney J. Douglas
Anatomical Neuropharmacology Unit,
Dept. Pharmacology,
Oxford, UK.
Abstract
I... | 717 |@word neurophysiology:2 version:1 middle:1 seems:1 proportion:1 proportionality:1 grey:1 gradual:1 linearized:4 thereby:1 solid:2 united:1 rightmost:1 current:30 comparing:1 activation:5 hyperpolarizing:1 realistic:1 distant:1 shape:1 motor:1 half:1 compo:1 mental:1 provides:1 node:4 location:2 contribute:1 sigmoi... |
6,821 | 7,170 | Learning Neural Representations of
Human Cognition across Many fMRI Studies
Arthur Mensch?
Inria
arthur.mensch@m4x.org
Julien Mairal?
Inria
julien.mairal@inria.fr
Danilo Bzdok
Department of Psychiatry, RWTH
danilo.bzdok@rwth-aachen.de
Bertrand Thirion?
Inria
bertrand.thirion@inria.fr
Ga?l Varoquaux?
Inria
gael.varo... | 7170 |@word multitask:1 trial:1 repository:4 mri:3 norm:2 coarseness:1 loading:2 open:1 d2:4 confirms:1 decomposition:4 euclidian:1 carry:2 reduction:24 initial:6 lorraine:1 series:1 score:1 selecting:1 halchenko:1 daniel:1 tuned:2 necessity:1 dubourg:1 existing:3 wd:7 com:1 activation:4 tackling:1 yet:3 diederik:1 bd:... |
6,822 | 7,171 | A KL-LUCB Bandit Algorithm for
Large-Scale Crowdsourcing
Ervin T?nczos? and Robert Nowak?
University of Wisconsin-Madison
tanczos@wisc.edu, rdnowak@wisc.edu
Bob Mankoff
Former Cartoon Editor of the New Yorker
bmankoff@hearst.com
Abstract
This paper focuses on best-arm identification in multi-armed bandits with bound... | 7171 |@word mild:1 trial:6 briefly:1 seems:1 c0:3 annoying:1 sg2:4 kalyanakrishnan:1 concise:1 yorker:15 offering:1 bootstrapped:3 past:1 existing:3 com:3 comparing:2 must:2 numerical:4 happen:1 designed:1 plot:3 update:1 v:3 implying:1 fewer:3 short:1 fa9550:1 characterization:1 provides:3 consulting:1 profound:1 shor... |
6,823 | 7,172 | Collaborative Deep Learning in
Fixed Topology Networks
Zhanhong Jiang1 , Aditya Balu1 , Chinmay Hegde2 , and Soumik Sarkar1
1
Department of Mechanical Engineering, Iowa State University,
zhjiang, baditya, soumiks@iastate.edu
2
Department of Electrical and Computer Engineering , Iowa State University, chinmay@iastate.e... | 7172 |@word hampson:1 private:1 version:2 cnn:1 norm:1 d2:1 simulation:1 tat:1 sgd:35 solid:2 harder:1 reduction:1 moment:5 liu:2 efficacy:1 daniel:1 denoting:1 kurt:1 outperforms:1 existing:2 current:1 comparing:1 com:1 activation:1 written:2 gpu:1 hajinezhad:1 fn:1 numerical:1 devin:2 informative:1 enables:6 plot:3 u... |
6,824 | 7,173 | Fast-Slow Recurrent Neural Networks
Asier Mujika
Department of Computer Science
ETH Z?rich, Switzerland
asierm@ethz.ch
Florian Meier
Department of Computer Science
ETH Z?rich, Switzerland
meierflo@inf.ethz.ch
Angelika Steger
Department of Computer Science
ETH Z?rich, Switzerland
steger@inf.ethz.ch
Abstract
Processin... | 7173 |@word nchen:1 compression:10 norm:3 jacob:1 hager:1 initial:2 contains:2 ours:6 subword:1 reynolds:1 outperforms:5 current:1 com:2 activation:1 diederik:1 written:3 john:2 ronald:2 subsequent:2 distant:1 wx:1 enables:1 plot:1 update:7 generative:2 fewer:1 discovering:1 ivo:2 ith:1 prize:11 short:5 vanishing:3 com... |
6,825 | 7,174 | Learning Disentangled Representations with
Semi-Supervised Deep Generative Models
N. Siddharth
University of Oxford
nsid@robots.ox.ac.uk
Brooks Paige
Alan Turing Institute
University of Cambridge
Jan-Willem van de Meent
Northeastern University
j.vandemeent@northeastern.edu
bpaige@turing.ac.uk
Alban Desmaison
Unive... | 7174 |@word kohli:4 middle:1 nd:3 pieter:1 seek:1 eng:1 covariance:2 decomposition:4 jacob:1 paid:1 sgd:1 solid:1 shading:1 moment:1 substitution:1 contains:1 efficacy:2 configuration:1 jimenez:2 daniel:2 series:1 ours:4 lightweight:1 fa8750:1 com:3 diederik:3 written:1 must:3 john:2 visible:1 enables:1 remove:1 treati... |
6,826 | 7,175 | Self-Supervised Intrinsic Image Decomposition
Michael Janner
MIT
Jiajun Wu
MIT
Tejas D. Kulkarni
DeepMind
janner@mit.edu
jiajunwu@mit.edu
tejasdkulkarni@gmail.com
Ilker Yildirim
MIT
Joshua B. Tenenbaum
MIT
ilkery@mit.edu
jbt@mit.edu
Abstract
Intrinsic decomposition from a single image is a highly challenging... | 7175 |@word kohli:1 repository:2 judgement:1 nonsensical:1 open:1 mehta:1 pieter:1 crucially:1 blender:4 decomposition:17 rgb:1 uncovers:1 shot:1 shading:55 takuya:2 initial:1 contains:1 deconvolutional:1 animated:1 past:2 com:1 comparing:1 activation:1 gmail:1 tackling:1 must:4 diederik:1 john:2 stemming:1 gavves:1 sh... |
6,827 | 7,176 | Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
Toyota Technological Institute at Chicago
{bneyshabur, srinadh, mcallester, nati}@ttic.edu
Abstract
With a goal of understanding what drives generalization in deep networks, we
consider several recently s... | 7176 |@word middle:6 polynomial:1 norm:90 twelfth:1 accounting:1 sgd:5 thereby:2 reduction:1 initial:1 score:2 current:1 wd:2 comparing:3 activation:4 yet:1 must:3 numerical:1 chicago:1 happen:1 plot:20 drop:2 update:1 v:2 alone:2 instantiate:1 beginning:1 provides:3 mannor:1 node:6 zhang:1 along:1 direct:1 qualitative... |
6,828 | 7,177 | A framework for Multi-A(rmed)/B(andit) Testing
with Online FDR Control
Fanny Yang
Dept. of EECS, U.C. Berkeley
fanny-yang@berkeley.edu
Aaditya Ramdas
Dept. of EECS and Statistics, U.C. Berkeley
ramdas@berkeley.edu
Kevin Jamieson
Allen School of CSE, U. of Washington
jamieson@cs.washington.edu
Martin Wainwright
Dept.... | 7177 |@word trial:6 briefly:1 version:2 seems:1 advantageous:1 proportion:3 open:1 termination:2 simulation:6 kalyanakrishnan:1 p0:2 concise:1 solid:1 yorker:2 initial:2 series:3 past:7 wainwrig:1 existing:2 comparing:1 com:1 yet:1 must:2 stine:2 shape:1 cis:1 drop:1 plot:3 update:1 n0:2 v:2 implying:1 intelligence:1 s... |
6,829 | 7,178 | Fader Networks:
Manipulating Images by Sliding Attributes
Guillaume Lample1,2 , Neil Zeghidour1,3 , Nicolas Usunier1 ,
Antoine Bordes1 , Ludovic Denoyer2 , Marc?Aurelio Ranzato1
{gl,neilz,usunier,abordes,ranzato}@fb.com
ludovic.denoyer@lip6.fr
Abstract
This paper introduces a new encoder-decoder architecture that is ... | 7178 |@word kohli:1 version:9 seems:1 open:2 cleanly:1 pieter:1 tried:1 jacob:1 harder:1 initial:1 liu:1 contains:3 score:3 exclusively:1 denoting:1 deconvolutional:1 pless:1 outperforms:2 current:5 com:2 luo:1 diederik:1 must:3 readily:1 written:1 john:1 realistic:4 shape:3 christian:1 interpretable:1 update:4 generat... |
6,830 | 7,179 | Action Centered Contextual Bandits
Kristjan Greenewald
Department of Statistics
Harvard University
kgreenewald@fas.harvard.edu
Ambuj Tewari
Department of Statistics
University of Michigan
tewaria@umich.edu
Predrag Klasnja
School of Information
University of Michigan
klasnja@umich.edu
Susan Murphy
Departments of Stat... | 7179 |@word trial:5 middle:2 d2:2 decomposition:1 tr:1 initial:1 contains:2 series:1 selecting:1 daniel:2 existing:1 current:3 contextual:46 nt:7 surprising:1 michal:1 chu:4 must:2 john:5 ronald:1 analytic:1 hypothesize:1 designed:1 interpretable:1 drop:1 update:2 v:1 stationary:6 generative:1 plot:1 device:2 intellige... |
6,831 | 718 | A Massively-Parallel SIMD Processor for
Neural Network and Machine Vision
Applications
Michael A. Glover
Current Technology, Inc.
99 Madbury Road
Durham, NH 03824
W. Thomas Miller, III
Department of Electrical and Computer Engineering
The University of New Hampshire
Durham, NH 03824
Abstract
This paper describes the... | 718 |@word coprocessor:1 duda:2 instruction:7 reduction:1 contains:1 score:1 tuned:1 current:2 must:1 node:13 sigmoidal:2 glover:4 direct:1 multi:1 automatically:1 actual:2 window:1 project:1 sparcstation:1 transformation:1 act:2 classifier:1 unit:4 grant:2 appear:1 declare:2 engineering:1 switching:2 onchip:1 programm... |
6,832 | 7,180 | Estimating Mutual Information for
Discrete-Continuous Mixtures
Sreeram Kannan
Department of Electrical Engineering
University of Washington
ksreeram@uw.edu
Weihao Gao
Department of ECE
Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
wgao9@illinois.edu
Sewoong Oh
Department of IESE
Coordinated... | 7180 |@word trial:1 milenkovic:1 middle:1 version:2 eliminating:1 faculty:1 reshef:2 clts:1 polynomial:1 simulation:2 crucially:1 covariance:1 zolt:1 reduction:1 liu:4 contains:2 efficacy:1 selecting:1 substitution:2 daniel:1 nonparanormal:1 outperforms:4 existing:1 ka:2 z2:1 scatter:2 written:1 grassberger:1 partition... |
6,833 | 7,181 | Attention Is All You Need
Ashish Vaswani?
Google Brain
avaswani@google.com
Llion Jones?
Google Research
llion@google.com
Noam Shazeer?
Google Brain
noam@google.com
Niki Parmar?
Google Research
nikip@google.com
Aidan N. Gomez? ?
University of Toronto
aidan@cs.toronto.edu
Jakob Uszkoreit?
Google Research
usz@google.... | 7181 |@word version:3 d2:3 crucially:1 excited:1 initial:1 configuration:1 contains:3 score:5 att:2 dff:1 tuned:1 ours:1 subword:1 outperforms:3 existing:1 com:8 activation:1 gmail:1 diederik:1 parmar:1 gpu:2 must:1 written:1 distant:1 subsequent:1 additive:3 christian:1 designed:2 interpretable:1 drop:1 bart:1 sukhbaa... |
6,834 | 7,182 | Recurrent Ladder Networks
Isabeau Pr?mont-Schwarz, Alexander Ilin, Tele Hotloo Hao,
Antti Rasmus, Rinu Boney, Harri Valpola
The Curious AI Company
{isabeau,alexilin,hotloo,antti,rinu,harri}@cai.fi
Abstract
We propose a recurrent extension of the Ladder networks [22] whose structure
is motivated by the inference requir... | 7182 |@word nd:1 propagate:2 tried:1 stateless:1 minus:2 moment:1 contains:3 fragment:1 score:3 tuned:1 ours:1 envision:1 past:3 existing:1 outperforms:1 z2:1 yet:1 must:1 written:1 realistic:1 visible:2 shape:2 occludes:1 designed:5 hourglass:1 update:4 polyphonic:4 cue:2 selected:1 generative:1 accordingly:1 dover:1 ... |
6,835 | 7,183 | Parameter-Free Online Learning via Model Selection
Dylan J. Foster
Cornell University
Satyen Kale
Google Research
Mehryar Mohri
NYU and Google Research
Karthik Sridharan
Cornell University
Abstract
We introduce an efficient algorithmic framework for model selection in online
learning, or parameter-free online learn... | 7183 |@word mild:2 version:2 briefly:2 polynomial:4 norm:26 stronger:1 open:1 mehta:1 jacob:1 incurs:5 thereby:1 boundedness:1 initial:1 configuration:1 series:1 chervonenkis:1 daniel:1 erven:3 recovered:2 contextual:2 nt:8 luo:1 tackling:1 universality:1 must:2 readily:2 john:1 additive:1 subsequent:1 gerchinovitz:1 u... |
6,836 | 7,184 | Bregman Divergence for Stochastic Variance
Reduction: Saddle-Point and Adversarial Prediction
Zhan Shi
Xinhua Zhang
University of Illinois at Chicago
Chicago, Illinois 60661
{zshi22,zhangx}@uic.edu
Yaoliang Yu
University of Waterloo
Waterloo, ON, N2L3G1
yaoliang.yu@uwaterloo.ca
Abstract
Adversarial machines, where a ... | 7184 |@word repository:1 norm:22 stronger:1 nd:2 seek:1 crucially:1 tried:5 covariance:1 innermost:1 pick:1 sgd:2 mention:2 arti:1 accommodate:1 reduction:8 initial:1 score:18 nally:1 current:1 written:5 chicago:2 partition:1 subsequent:1 cant:3 enables:1 remove:2 update:13 juditsky:1 generative:2 half:2 intelligence:1... |
6,837 | 7,185 | Unbounded cache model for online language
modeling with open vocabulary
Edouard Grave
Facebook AI Research
egrave@fb.com
Moustapha Cisse
Facebook AI Research
moustaphacisse@fb.com
Armand Joulin
Facebook AI Research
ajoulin@fb.com
Abstract
Recently, continuous cache models were proposed as extensions to recurrent ne... | 7185 |@word multitask:3 armand:1 version:2 briefly:4 compression:3 retraining:4 bptt:1 underline:1 open:8 dramatic:1 shot:3 accommodate:1 recursively:1 reduction:2 initial:1 contains:3 document:1 interestingly:2 past:5 existing:3 current:3 com:4 activation:2 must:2 distant:1 cis:1 designed:3 update:3 v:1 discrimination... |
6,838 | 7,186 | Predictive State Recurrent Neural Networks
Carlton Downey
Carnegie Mellon University
Pittsburgh, PA 15213
cmdowney@cs.cmu.edu
Ahmed Hefny
Carnegie Mellon University
Pittsburgh, PA, 15213
ahefny@cs.cmu.edu
Byron Boots
Georgia Tech
Atlanta, GA, 30332
bboots@cc.gatech.edu
Boyue Li
Carnegie Mellon University
Pittsburgh,... | 7186 |@word repository:2 version:1 norm:3 nd:1 bptt:16 bf:1 open:1 pieter:1 simulation:1 crucially:2 decomposition:14 contraction:1 q1:3 pressure:2 thereby:1 kbr:1 recursively:1 moment:4 initial:4 series:2 hereafter:1 selecting:1 daniel:1 ours:2 rkhs:1 outperforms:1 existing:6 current:5 com:2 activation:1 yet:1 dx:2 su... |
6,839 | 7,187 | Early stopping for kernel boosting algorithms: A
general analysis with localized complexities
Yuting Wei1
Fanny Yang2?
Martin J. Wainwright1,2
Department of Statistics1
Department of Electrical Engineering and Computer Sciences2
UC Berkeley
Berkeley, CA 94720
{ytwei, fanny-yang, wainwrig}@berkeley.edu
Abstract
Early ... | 7187 |@word trial:1 illustrating:1 achievable:1 norm:2 polynomial:6 open:1 closure:1 simulation:4 boundedness:5 series:3 renewed:1 rkhs:6 past:3 wainwrig:1 comparing:1 must:2 subsequent:1 additive:2 numerical:4 plot:9 update:10 intelligence:1 greedy:1 accordingly:1 beginning:1 supx2x:1 characterization:1 boosting:34 pr... |
6,840 | 7,188 | SVCCA: Singular Vector Canonical Correlation
Analysis for Deep Learning Dynamics and
Interpretability
Maithra Raghu,1,2 Justin Gilmer,1 Jason Yosinski,3 & Jascha Sohl-Dickstein1
1
Google Brain 2 Cornell University 3 Uber AI Labs
maithrar@gmail?com, gilmer@google?com, yosinski@uber?com, jaschasd@google?com
Abstract
We... | 7188 |@word version:1 compression:5 seems:1 norm:2 retraining:1 logit:1 open:1 grey:2 bn:9 decomposition:3 covariance:5 solid:1 reduction:3 com:4 comparing:3 activation:6 gmail:1 intriguing:1 john:1 concatenate:1 subsequent:1 wx:1 visible:1 net1:3 cheap:1 christian:1 plot:5 progressively:1 v:1 aside:1 hwd:1 parameteriz... |
6,841 | 7,189 | Convolutional Phase Retrieval
Qing Qu
Columbia University
qq2105@columbia.edu
Yuqian Zhang
Columbia University
yz2409@columbia.edu
John Wright
Columbia University
jw2966@columbia.edu
Yonina C. Eldar
Technion
yonina@ee.technion.ac.il
Abstract
We study the convolutional phase retrieval problem, which considers recover... | 7189 |@word briefly:1 polynomial:10 seems:1 norm:1 c0:4 calculus:2 seek:2 rgb:1 contraction:5 decomposition:1 delgado:1 shechtman:1 moment:4 cyclic:3 gagliardi:1 ours:1 past:1 existing:6 csn:12 recovered:1 imaginary:1 attracted:1 must:1 john:6 numerical:3 benign:2 mordechai:1 designed:1 plot:1 update:2 resampling:1 few... |
6,842 | 719 | Central and Pairwise Data Clustering by
Competitive Neural Networks
Joachim Buhmann & Thomas Hofmann
Rheinische Friedrich-Wilhelms-UniversiHit
Institut fiir Informatik II, RomerstraBe 164
D-53117 Bonn, Fed. Rep. Germany
Abstract
Data clustering amounts to a combinatorial optimization problem to reduce the complexity ... | 719 |@word compression:8 covariance:1 euclidian:6 thereby:2 harder:1 reduction:1 configuration:4 current:1 com:1 written:1 transcendental:2 partition:2 hofmann:4 remove:2 xk:1 vanishing:1 short:1 provides:1 quantized:2 codebook:1 complication:1 node:2 quantizer:1 ik:1 cta:1 pairwise:19 expected:4 mechanic:1 ry:2 brain:... |
6,843 | 7,190 | Learning Non-Gaussian Multi-Index Model via
Second-Order Stein?s Method
Zhuoran Yang? Krishna Balasubramanian? Zhaoran Wang? Han Liu?
Abstract
We consider estimating the parametric components of semiparametric multi-index
models in high dimensions. To bypass the requirements of Gaussianity or elliptical
symmetry of co... | 7190 |@word mild:1 trial:1 version:1 polynomial:2 norm:8 seems:1 c0:3 d2:3 simulation:3 crucially:1 r:1 covariance:2 p0:8 solid:1 moment:7 reduction:5 liu:13 contains:4 score:16 xinyang:1 existing:5 elliptical:3 com:1 current:1 surprising:1 activation:1 gmail:1 intriguing:1 john:1 numerical:1 hanie:2 shape:2 enables:4 ... |
6,844 | 7,191 | Gaussian Quadrature for Kernel Features
Tri Dao
Department of Computer Science
Stanford University
Stanford, CA 94305
trid@stanford.edu
Christopher De Sa
Department of Computer Science
Cornell University
Ithaca, NY 14853
cdesa@cs.cornell.edu
Christopher R?
Department of Computer Science
Stanford University
Stanford, ... | 7191 |@word middle:1 polynomial:10 nd:1 d2:1 km:2 decomposition:2 pick:1 nystr:2 stitson:1 liu:3 series:2 quo:1 selecting:1 daniel:2 document:2 fa8750:3 existing:1 comparing:1 com:1 jaz:1 attracted:1 written:2 john:2 griebel:1 numerical:8 ldc93s1:1 hofmann:1 enables:1 plot:2 designed:1 sponsored:1 v:5 half:1 fewer:4 se... |
6,845 | 7,192 | Value Prediction Network
Junhyuk Oh?
Satinder Singh?
Honglak Lee?,?
University of Michigan
?
Google Brain
{junhyuk,baveja,honglak}@umich.edu, honglak@google.com
?
Abstract
This paper proposes a novel deep reinforcement learning (RL) architecture, called
Value Prediction Network (VPN), which integrates model-free and ... | 7192 |@word cnn:3 exploitation:1 polynomial:1 suitably:1 termination:2 r:1 decomposition:2 q1:2 harder:1 recursively:2 initial:3 score:1 bootstrapped:4 interestingly:1 outperforms:5 imaginary:1 existing:3 o2:1 com:2 current:1 steiner:1 mishra:1 hasselt:2 freitas:1 guez:2 devin:1 resent:1 enables:1 hypothesize:2 update:... |
6,846 | 7,193 | A Learning Error Analysis for Structured Prediction
with Approximate Inference
1
Yuanbin Wu1, 2 , Man Lan1, 2 , Shiliang Sun1 , Qi Zhang3 , Xuanjing Huang3
School of Computer Science and Software Engineering, East China Normal University
2
Shanghai Key Laboratory of Multidimensional Information Processing
3
School of... | 7193 |@word msr:2 norm:2 proportion:1 dekel:1 heuristically:3 underperform:1 covariance:1 concise:1 configuration:1 contains:5 score:5 series:1 tuned:2 document:1 outperforms:1 existing:2 comparing:2 trustworthy:1 chu:3 must:1 parsing:16 john:3 deniz:1 shawetaylor:1 greedy:4 generative:1 amir:3 plane:2 smith:1 coarse:1... |
6,847 | 7,194 | Efficient Second-Order Online Kernel
Learning with Adaptive Embedding
Daniele Calandriello
Alessandro Lazaric
Michal Valko
SequeL team, INRIA Lille - Nord Europe, France
{daniele.calandriello, alessandro.lazaric, michal.valko}@inria.fr
Abstract
Online kernel learning (OKL) is a flexible framework for prediction probl... | 7194 |@word version:2 inversion:1 polynomial:1 replicate:1 dekel:1 open:1 covariance:3 decomposition:1 pengcheng:1 sgd:5 nystr:10 tr:2 reaping:1 reduction:2 liu:1 contains:1 score:5 woodruff:1 rkhs:14 outperforms:2 existing:2 current:3 comparing:1 michal:3 skipping:1 luo:2 yet:1 john:1 distant:1 realistic:1 numerical:1... |
6,848 | 7,195 | Implicit Regularization in Matrix Factorization
Suriya Gunasekar
TTI at Chicago
suriya@ttic.edu
Blake Woodworth
TTI at Chicago
blake@ttic.edu
Behnam Neyshabur
TTI at Chicago
behnam@ttic.edu
Srinadh Bhojanapalli
TTI at Chicago
srinadh@ttic.edu
Nathan Srebro
TTI at Chicago
nati@ttic.edu
Abstract
We study implicit re... | 7195 |@word version:1 norm:46 seems:2 open:1 closure:2 simulation:1 commute:9 sepulchre:1 initial:2 contains:1 interestingly:1 existing:1 ka:1 discretization:2 surprising:2 yet:1 additonally:1 must:1 numerical:6 chicago:5 discernible:1 plot:7 update:2 characterization:1 coarse:1 theodoros:1 zhang:1 u2i:1 mathematical:1... |
6,849 | 7,196 | Optimal Shrinkage of Singular Values Under
Random Data Contamination
Matan Gavish
School of Computer Science and Engineering
Hebrew University
Jerusalem, Israel
gavish@cs.huji.ac.il
Danny Barash
School of Computer Science and Engineering
Hebrew University
Jerusalem, Israel
danny.barash@mail.huji.ac.il
Abstract
A low ... | 7196 |@word version:1 norm:4 nd:1 simulation:5 crucially:1 bn:3 decomposition:8 covariance:3 minming:1 moment:2 reduction:2 liu:1 series:1 interestingly:1 outperforms:1 existing:2 recovered:1 com:1 luo:2 yet:2 danny:2 must:3 john:3 additive:21 numerical:1 shape:4 designed:2 plot:1 alone:1 intelligence:2 prohibitive:1 p... |
6,850 | 7,197 | Countering Feedback Delays in Multi-Agent Learning
Zhengyuan Zhou
Stanford University
zyzhou@stanford.edu
Nicholas Bambos
Stanford University
bambos@stanford.edu
Panayotis Mertikopoulos
Univ. Grenoble Alpes, CNRS, Inria, LIG
panayotis.mertikopoulos@imag.fr
Peter Glynn
Stanford University
glynn@stanford.edu
Claire T... | 7197 |@word exploitation:1 stronger:1 norm:8 yi0:2 open:2 hu:1 git:6 prominence:1 mention:1 thereby:3 accommodate:1 shot:1 initial:7 contains:3 series:1 genetic:1 past:4 existing:1 current:3 universality:1 written:4 bd:1 conforming:2 must:2 hou:2 synchronicity:1 timestamps:1 happen:1 update:11 congestion:2 kyk:1 warmut... |
6,851 | 7,198 | Asynchronous Coordinate Descent under More
Realistic Assumption?
Tao Sun
National University of Defense Technology
Changsha, Hunan 410073, China
nudtsuntao@163.com
Robert Hannah
University of California, Los Angeles
Los Angeles, CA 90095, USA
RobertHannah89@math.ucla.edu
Wotao Yin
University of California, Los Angel... | 7198 |@word mild:1 version:2 eliminating:2 stronger:2 norm:1 c0:7 pick:1 sgd:1 cyclic:9 contains:1 liu:2 ours:2 current:6 com:1 discretization:1 leblond:1 assigning:1 must:5 numerical:1 realistic:2 treating:1 update:29 greedy:1 selected:1 intelligence:1 xk:46 beginning:1 ith:1 core:2 math:2 node:4 bittorf:1 zhang:1 unb... |
6,852 | 7,199 | Linear Convergence of a Frank-Wolfe Type
Algorithm over Trace-Norm Balls?
Zeyuan Allen-Zhu
Microsoft Research, Redmond
zeyuan@csail.mit.edu
Wei Hu
Princeton University
huwei@cs.princeton.edu
Elad Hazan
Princeton University
ehazan@cs.princeton.edu
Yuanzhi Li
Princeton University
yuanzhil@cs.princeton.edu
Abstract
We ... | 7199 |@word h:2 version:11 polynomial:6 norm:19 stronger:2 nd:1 h2t:1 hu:1 km:1 confirms:1 grey:1 decomposition:6 tr:2 contains:2 frankwolfe:1 outperforms:1 current:1 ka:2 activation:2 yet:1 written:1 additive:1 zaid:1 plot:5 update:1 half:1 selected:1 theoretician:1 characterization:1 iterates:1 org:1 unbounded:1 math... |
6,853 | 72 | 223
'Ensemble' Boltzmann Units
have Collective Computational Properties
like those of Hopfield and Tank Neurons
Mark Derthick and Joe Tebelskis
Department of Computer Science
Carnegie-Mellon University
1 Introduction
There are three existing connection::;t models in which network states are assigned
a computational e... | 72 |@word trial:2 seems:5 stronger:1 r:1 harder:1 initial:3 contains:1 subjective:1 existing:1 current:1 merrick:1 si:1 yet:2 must:8 numerical:1 informative:2 shape:1 designed:1 sponsored:1 depict:1 update:1 tenn:1 half:1 intelligence:1 beginning:1 steepest:2 ith:1 lr:2 location:2 sigmoidal:1 rc:1 along:2 direct:2 diff... |
6,854 | 720 | Bayesian Backprop in Action:
Pruning, Committees, Error Bars
and an Application to Spectroscopy
Hans Henrik Thodberg
Danish Meat Research Institute
Maglegaardsvej 2, DK-4000 Roskilde
thodberg~nn.meatre.dk
Abstract
MacKay's Bayesian framework for backpropagation is conceptually
appealing as well as practical. It autom... | 720 |@word cu:1 pick:1 absorbance:1 initial:1 configuration:1 contains:2 pub:1 tuned:5 comparing:1 must:4 designed:1 selected:2 reappears:1 compo:1 firstly:1 direct:1 consists:1 underfitting:1 automatically:1 actual:1 what:2 interpreted:1 every:2 act:2 fat:3 unit:14 negligible:2 id:1 logo:2 uwu:1 mateo:1 suggests:1 co:... |
6,855 | 7,200 | Hierarchical Clustering Beyond the Worst-Case
Vincent Cohen-Addad
University of Copenhagen
vcohenad@gmail.com
Varun Kanade
University of Oxford
Alan Turing Institute
varunk@cs.ox.ac.uk
Frederik Mallmann-Trenn
MIT
mallmann@mit.edu
Abstract
Hiererachical clustering, that is computing a recursive partitioning of a dat... | 7200 |@word repository:2 seems:1 stronger:1 rajaraman:1 p0:3 pick:1 euclidian:1 tr:4 tci1:2 recursively:1 series:1 document:1 semirandom:1 com:1 gmail:1 must:2 john:1 stemming:1 numerical:1 partition:2 happen:1 kdd:1 noche:1 seeding:2 drop:2 update:1 n0:1 leaf:20 guess:2 nq:8 sys:2 ith:1 provides:1 completeness:1 node:... |
6,856 | 7,201 | Invariance and Stability
of Deep Convolutional Representations
Julien Mairal
Inria?
julien.mairal@inria.fr
Alberto Bietti
Inria?
alberto.bietti@inria.fr
Abstract
In this paper, we study deep signal representations that are near-invariant to groups
of transformations and stable to the action of diffeomorphisms withou... | 7201 |@word mild:1 deformed:1 cnn:5 version:1 msr:1 polynomial:3 norm:33 rgb:1 covariance:3 p0:2 bn:2 commute:5 nystr:1 harder:1 recursively:1 carry:1 initial:2 contains:6 rkhs:17 interestingly:1 kmk:3 recovered:2 discretization:6 activation:8 yet:3 written:1 ckns:4 additive:1 subsequent:2 realistic:1 shape:2 wx:1 remo... |
6,857 | 7,202 | Statistical Cost Sharing
Eric Balkanski
Harvard University
ericbalkanski@g.harvard.edu
Umar Syed
Google NYC
usyed@google.com
Sergei Vassilvitskii
Google NYC
sergeiv@google.com
Abstract
We study the cost sharing problem for cooperative games in situations where the
cost function C is not available via oracle queries,... | 7202 |@word private:1 version:1 fatima:3 polynomial:5 stronger:2 norm:3 d2:5 confirms:1 hu:1 essay:1 wexler:2 asks:2 contains:1 current:1 com:2 surprising:1 si:3 intriguing:1 sergei:1 must:2 attracted:1 written:3 readily:1 additive:1 john:1 christian:2 alone:1 half:2 intelligence:2 device:2 assurance:1 core:71 junta:1 ... |
6,858 | 7,203 | The Expressive Power of Neural Networks: A View
from the Width
Zhou Lu1,3
1400010739@pku.edu.cn
Hongming Pu1
1400010621@pku.edu.cn
Zhiqiang Hu2
huzq@pku.edu.cn
Feicheng Wang1,3
1400010604@pku.edu.cn
Liwei Wang2,3
wanglw@cis.pku.edu.cn
1, Department of Mathematics, Peking University
2, Key Laboratory of Machine Per... | 7203 |@word version:2 polynomial:23 seems:2 stronger:2 open:3 chopping:1 bn:1 reduction:2 celebrated:1 series:4 contains:1 liu:1 tuned:1 kurt:1 existing:1 current:2 comparing:1 activation:6 dx:6 must:3 christian:1 designed:1 v:2 half:1 funahashi:2 record:1 node:14 firstly:1 sigmoidal:1 zhang:1 mathematical:1 along:1 co... |
6,859 | 7,204 | Spectrally-normalized margin bounds
for neural networks
Peter L. Bartlett?
Dylan J. Foster?
Matus Telgarsky?
Abstract
This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized spectral complexity: their Lipschitz
constant, meaning the product of t... | 7204 |@word version:2 briefly:1 norm:35 seems:3 open:3 queensland:1 incurs:1 sgd:7 minus:1 harder:3 reduction:1 configuration:1 interestingly:3 pna:1 existing:1 recovered:1 ka:9 comparing:1 com:1 si:1 must:1 gpu:2 visible:1 subsequent:1 christian:1 plot:4 depict:1 v:1 alone:1 instantiate:1 ith:1 farther:1 provides:1 bo... |
6,860 | 7,205 | Robust and Efficient Transfer Learning with Hidden
Parameter Markov Decision Processes
Taylor Killian?
taylorkillian@g.harvard.edu
Harvard University
Samuel Daulton?
sdaulton@g.harvard.edu
Harvard University, Facebook?
George Konidaris
gdk@cs.brown.edu
Brown University
Finale Doshi-Velez
finale@seas.harvard.edu
Harv... | 7205 |@word multitask:1 version:1 pw:4 nd:2 tadepalli:2 km:1 integrative:1 simulation:1 decomposition:1 covariance:2 citeseer:1 sgd:2 incurs:1 ld:1 moment:1 bai:3 ndez:4 contains:1 liu:1 initial:5 outperforms:2 hasselt:1 current:8 com:1 nt:2 guez:1 must:1 devin:1 subsequent:1 predetermined:1 enables:2 update:8 stationa... |
6,861 | 7,206 | Population Matching Discrepancy and
Applications in Deep Learning
Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu?
Dept. of Comp. Sci. & Tech., TNList Lab, State Key Lab for Intell. Tech. & Sys.
Tsinghua University, Beijing, 100084, China
{chenjian14,licx14,ruyz13}@mails.tsinghua.edu.cn, dcszj@tsinghua.edu.cn
Abstrac... | 7206 |@word kulis:1 version:1 middle:1 polynomial:1 stronger:4 norm:2 villani:1 propagate:1 citeseer:1 sgd:8 tnlist:1 moment:7 venkatasubramanian:1 contains:1 score:1 selecting:1 jimenez:2 daniel:1 rkhs:1 document:2 deconvolutional:1 outperforms:4 comparing:4 nt:1 surprising:1 com:1 activation:1 ddc:1 dx:1 must:2 gpu:5... |
6,862 | 7,207 | Scalable Planning with Tensorflow for Hybrid
Nonlinear Domains
Ga Wu
Buser Say
Scott Sanner
Department of Mechanical & Industrial Engineering, University of Toronto, Canada
email: {wuga,bsay,ssanner}@mie.utoronto.ca
Abstract
Given recent deep learning results that demonstrate the ability to effectively optimize high... | 7207 |@word h:1 trial:1 version:8 nd:1 additively:1 jacob:1 sgd:3 reduction:3 moment:1 initial:5 efficacy:1 score:1 daniel:1 denoting:2 outperforms:3 existing:2 steiner:1 discretization:1 comparing:1 si:5 guez:1 must:1 gpu:6 john:2 devin:1 visible:1 numerical:1 ronald:1 designed:4 update:7 overshooting:2 half:1 isard:1... |
6,863 | 7,208 | Boltzmann Exploration Done Right
Nicol? Cesa-Bianchi
Universit? degli Studi di Milano
Milan, Italy
nicolo.cesa-bianchi@unimi.it
Claudio Gentile
INRIA Lille ? Nord Europe
Villeneuve d?Ascq, France
cla.gentile@gmail.com
G?bor Lugosi
ICREA & Universitat Pompeu Fabra
Barcelona, Spain
gabor.lugosi@gmail.com
Gergely Neu
... | 7208 |@word exploitation:1 version:2 illustrating:1 nd:1 rigged:1 citeseer:1 cla:1 pick:1 mention:1 moment:3 initial:2 tuned:2 interestingly:1 past:1 com:3 nt:11 gmail:3 dx:2 written:1 drop:1 update:1 greedy:3 intelligence:2 beginning:4 short:1 revisited:1 org:2 c22:1 bge:3 c2:8 symposium:1 competitiveness:1 prove:1 no... |
6,864 | 7,209 | Learned in Translation: Contextualized Word Vectors
Bryan McCann
bmccann@salesforce.com
James Bradbury
james.bradbury@salesforce.com
Caiming Xiong
cxiong@salesforce.com
Richard Socher
rsocher@salesforce.com
Abstract
Computer vision has benefited from initializing multiple deep layers with weights
pretrained on larg... | 7209 |@word kulis:1 cnn:7 version:5 briefly:1 stronger:1 open:4 d2:1 tr:3 tice:2 carry:1 liu:1 contains:5 score:7 ours:8 document:3 current:1 com:5 transferability:1 comparing:1 activation:4 yet:1 dx:1 parsing:1 subsequent:1 concatenate:1 wx:5 hypothesize:1 treating:1 designed:1 alone:1 half:1 discovering:1 aglar:1 sho... |
6,865 | 721 | Unsupervised Parallel Feature Extraction
from First Principles
..
Mats Osterberg
Image Processing Laboratory
Dept. EE., Linkoping University
S-58183 Linkoping Sweden
Reiner Lenz
Image Processing Laboratory
Dept. EE., Linkoping University
S-58183 Linkoping Sweden
Abstract
We describe a number of learning rules that ... | 721 |@word determinant:4 briefly:1 version:1 norm:1 tried:1 covariance:2 solid:2 reduction:1 moment:1 initial:2 contains:1 series:1 protection:1 scatter:2 realize:1 numerical:2 plasticity:1 designed:1 atlas:1 discrimination:3 selected:2 iso:1 steepest:4 colored:1 node:1 lor:1 consists:1 fitting:1 ra:1 examine:1 ol:1 be... |
6,866 | 7,210 | Neural Discrete Representation Learning
Aaron van den Oord
DeepMind
avdnoord@google.com
Oriol Vinyals
DeepMind
vinyals@google.com
Koray Kavukcuoglu
DeepMind
korayk@google.com
Abstract
Learning useful representations without supervision remains a key challenge in
machine learning. In this paper, we propose a simple ... | 7210 |@word middle:2 unaltered:1 compression:7 pieter:2 confirms:1 hu:1 covariance:1 shot:2 reduction:2 initial:2 configuration:1 fragment:1 jimenez:4 daniel:1 offering:1 tuned:1 panayotov:1 deconvolutional:2 current:1 com:3 analysed:1 activation:2 yet:1 diederik:4 john:2 realistic:1 christian:1 interpretable:1 update:... |
6,867 | 7,211 | Generalizing GANs: A Turing Perspective
Roderich Gro? and Yue Gu
Department of Automatic Control and Systems Engineering
The University of Sheffield
{r.gross,ygu16}@sheffield.ac.uk
Wei Li
Department of Electronics
The University of York
wei.li@york.ac.uk
Melvin Gauci
Wyss Institute for Biologically Inspired Engineeri... | 7211 |@word trial:9 cylindrical:2 version:1 toggling:1 judgement:2 disk:1 open:1 termination:2 d2:2 confirms:1 simulation:8 simplifying:2 electronics:1 configuration:14 contains:1 hereafter:2 genetic:3 outperforms:1 existing:1 current:1 com:1 yet:1 must:1 realistic:2 subsequent:1 motor:2 remove:1 drop:1 designed:1 upda... |
6,868 | 7,212 | Scalable Log Determinants for Gaussian Process
Kernel Learning
Kun Dong 1 , David Eriksson 1 , Hannes Nickisch 2 , David Bindel 1 , Andrew Gordon Wilson 1
1
Cornell University, 2 Phillips Research Hamburg
Abstract
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphi... | 7212 |@word determinant:30 cox:4 repository:1 polynomial:3 norm:1 consequential:2 termination:1 willing:1 hu:2 simulation:1 covariance:9 decomposition:5 q1:2 concise:1 tr:11 moment:2 bai:1 series:4 score:1 daniel:2 renewed:1 existing:1 elliptical:1 current:1 com:3 recovered:3 dx:1 must:2 readily:1 determinantal:3 gpu:1... |
6,869 | 7,213 | Poincar? Embeddings for
Learning Hierarchical Representations
Maximilian Nickel
Facebook AI Research
maxn@fb.com
Douwe Kiela
Facebook AI Research
dkiela@fb.com
Abstract
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, state-of-the-art embeddi... | 7213 |@word armand:2 briefly:1 version:2 norm:6 nd:2 disk:2 open:2 adrian:1 calculus:1 closure:5 essay:1 eng:1 hyponym:1 automat:1 mammal:2 initial:2 score:8 tuned:1 subword:1 outperforms:1 existing:1 com:2 mari:1 si:1 yet:1 john:1 wup:1 distant:1 numerical:1 visible:1 remove:1 update:5 intelligence:4 greedy:2 half:1 l... |
6,870 | 7,214 | Learning Combinatorial Optimization Algorithms over Graphs
Hanjun Dai? , Elias B. Khalil?, Yuyu Zhang, Bistra Dilkina, Le Song
College of Computing, Georgia Institute of Technology
{hanjun.dai, elias.khalil, yuyu.zhang, bdilkina, lsong}@cc.gatech.edu
Abstract
The design of good heuristics or approximation algorithms ... | 7214 |@word trial:3 middle:1 version:2 polynomial:2 stronger:2 nd:5 tedious:1 open:2 termination:9 willing:1 propagate:1 tried:1 decomposition:1 pick:1 sgd:1 euclidian:1 recursively:2 carry:1 memetracker:1 uncovered:2 score:1 selecting:1 reynolds:1 outperforms:1 existing:3 freitas:2 current:9 com:3 ka:1 manuel:2 si:2 t... |
6,871 | 7,215 | Robust Conditional Probabilities
Yoav Wald
School of Computer Science and Engineering
Hebrew University
yoav.wald@mail.huji.ac.il
Amir Globerson
The Balvatnik School of Computer Science
Tel-Aviv University
gamir@mail.tau.ac.il
Abstract
Conditional probabilities are a core concept in machine learning. For example,
opt... | 7215 |@word version:3 polynomial:7 logit:1 mezuman:1 seek:1 pick:1 minus:1 reduction:1 moment:11 cyclic:3 contains:2 charniak:1 rkhs:3 ours:1 bhattacharyya:1 current:1 surprising:1 activation:3 yet:1 universality:1 parsing:2 partition:1 informative:3 remove:1 plot:1 v:1 intelligence:2 generative:1 yr:1 item:1 amir:1 ac... |
6,872 | 7,216 | Learning with Bandit Feedback in Potential Games
Johanne Cohen
LRI-CNRS, Universit? Paris-Sud,Universit? Paris-Saclay, France
johanne.cohen@lri.fr
Am?lie H?liou
LIX, Ecole Polytechnique, CNRS, AMIBio, Inria, Universit? Paris-Saclay
amelie.heliou@polytechnique.edu
Panayotis Mertikopoulos
Univ. Grenoble Alpes, CNRS, Inr... | 7216 |@word mild:1 version:1 stronger:2 logit:3 suitably:1 mehta:2 unif:8 rigged:1 linearized:1 moment:3 initial:3 ftrl:1 score:7 exclusively:1 ecole:1 denoting:1 precluding:1 genetic:1 existing:1 comparing:1 luo:1 must:3 readily:1 fn:8 happen:1 ligett:1 update:10 congestion:6 stationary:2 implying:1 isotropic:1 vanish... |
6,873 | 7,217 | Multi-Agent Actor-Critic for Mixed
Cooperative-Competitive Environments
Ryan Lowe?
McGill University
OpenAI
Jean Harb
McGill University
OpenAI
Yi Wu?
UC Berkeley
Aviv Tamar
UC Berkeley
Pieter Abbeel
UC Berkeley
OpenAI
Igor Mordatch
OpenAI
Abstract
We explore deep reinforcement learning methods for multi-agent dom... | 7217 |@word kohli:1 private:1 middle:2 stronger:1 twelfth:1 unif:1 pieter:1 grey:1 simulation:1 hu:1 jacob:1 pg:1 q1:1 thereby:1 recursively:1 initial:2 configuration:1 contains:1 score:2 ours:1 past:1 existing:2 outperforms:2 freitas:1 com:5 gmail:1 scatter:1 must:13 written:2 guez:1 periodically:1 remove:1 designed:1... |
6,874 | 7,218 | Communication-Efficient Distributed Learning
of Discrete Probability Distributions
Ilias Diakonikolas
CS, USC
diakonik@usc.edu
Abhiram Natarajan
CS, Purdue
nataraj2@purdue.edu
Elena Grigorescu
CS, Purdue
elena-g@purdue.edu
Jerry Li
EECS & CSAIL, MIT
jerryzli@mit.edu
Krzysztof Onak
IBM Research, NY
konak@us.ibm.com
... | 7218 |@word kong:1 briefly:1 version:8 polynomial:2 norm:15 seems:1 faculty:1 open:1 hu:1 vldb:1 seek:1 crucially:3 reduction:4 venkatasubramanian:2 celebrated:1 selecting:1 chervonenkis:2 woodruff:3 prefix:2 recovered:1 com:1 current:1 must:6 visible:1 partition:8 additive:1 n0:1 half:2 fewer:2 selected:1 intelligence... |
6,875 | 7,219 | Simple and Scalable Predictive Uncertainty
Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel Charles Blundell
DeepMind
{balajiln,apritzel,cblundell}@google.com
Abstract
Deep neural networks (NNs) are powerful black box predictors that have recently
achieved impressive performance on a wide spe... | 7219 |@word illustrating:1 compression:1 seems:2 norm:1 crucially:1 tried:1 forecaster:1 jacob:1 pick:1 thereby:1 shot:1 harder:1 liu:1 series:3 score:9 contains:2 hoiem:1 tram:2 tuned:1 ours:1 interestingly:3 bootstrapped:1 subjective:1 outperforms:2 existing:1 current:1 com:1 comparing:4 surprising:1 nowlan:1 yet:5 i... |
6,876 | 722 | Structural and Behavioral Evolution
of Recurrent Networks
Gregory M. Saunders, Peter J. Angeline, and Jordan B. Pollack
Laboratory for Artificial Intelligence Research
Department of Computer and Information Science
The Ohio State University
Columbus, Ohio 43210
saunders@cis.ohio-state.edu
Abstract
This paper introduce... | 722 |@word illustrating:1 middle:2 korf:1 solid:1 initial:3 exclusively:1 efficacy:1 angeline:7 genetic:9 cleared:3 activation:2 must:1 john:1 remove:1 progressively:1 intelligence:2 selected:1 rp1:1 supplying:1 num:6 coarse:1 node:24 behavioral:5 behavior:7 brain:2 relying:1 company:1 food:17 endlessly:1 becomes:1 beg... |
6,877 | 7,220 | When Worlds Collide: Integrating Different
Counterfactual Assumptions in Fairness
Chris Russell?
The Alan Turing Institute and
University of Surrey
crussell@turing.ac.uk
Matt J. Kusner?
The Alan Turing Institute and
University of Warwick
mkusner@turing.ac.uk
Joshua R. Loftus?
New York University
loftus@nyu.edu
Rica... | 7220 |@word trial:1 briefly:1 polynomial:1 stronger:2 justice:3 sex:5 willing:1 decomposition:1 jacob:1 harder:1 born:2 contains:2 score:5 configuration:1 series:1 bc:1 sendhil:1 longitudinal:1 pless:1 bilal:1 existing:1 manuel:1 must:3 applicant:1 realistic:1 additive:1 informative:1 entrance:2 plot:2 propublica:3 upd... |
6,878 | 7,221 | Matrix Norm Estimation from a Few Entries
Ashish Khetan
Department of ISE
University of Illinois Urbana-Champaign
khetan2@illinois.edu
Sewoong Oh
Department of ISE
University of Illinois Urbana-Champaign
swoh@illinois.edu
Abstract
Singular values of a data in a matrix form provide insights on the structure of
the dat... | 7221 |@word kong:1 determinant:1 faculty:1 polynomial:5 norm:30 stronger:1 km:26 d2:16 seek:1 crucially:1 covariance:2 decomposition:2 tr:6 solid:1 moment:1 reduction:1 cyclic:11 woodruff:2 ours:1 khetan:1 outperforms:2 existing:1 recovered:1 comparing:1 com:1 scatter:1 readily:1 numerical:4 partition:1 plot:1 short:1 ... |
6,879 | 7,222 | Neural Networks for Efficient Bayesian Decoding of
Natural Images from Retinal Neurons
Nikhil Parthasarathy?
Stanford University
nikparth@gmail.com
Thomas Rutten
Columbia University
tkr2112@columbia.edu
Eleanor Batty?
Columbia University
erb2180@columbia.edu
Mohit Rajpal
Columbia University
mr3522@columbia.edu
Willi... | 7222 |@word neurophysiology:2 blindness:1 trial:2 version:8 middle:1 polynomial:1 hippocampus:1 seems:1 simulation:3 pulse:1 inpainting:4 ld:9 reduction:1 liu:2 daniel:2 tuned:3 outperforms:3 existing:1 subjective:1 recovered:1 com:1 current:2 comparing:5 ka:1 activation:1 gmail:1 diederik:1 laparra:1 luo:1 realistic:2... |
6,880 | 7,223 | Causal Effect Inference with
Deep Latent-Variable Models
Christos Louizos
University of Amsterdam
TNO Intelligent Imaging
c.louizos@uva.nl
Uri Shalit
New York University
CIMS
uas1@nyu.edu
David Sontag
Massachusetts Institute of Technology
CSAIL & IMES
dsontag@mit.edu
Joris Mooij
University of Amsterdam
j.m.mooij@uv... | 7223 |@word trial:2 briefly:1 inversion:1 almond:1 johansson:3 yi0:1 paredes:1 sex:2 calculus:1 decomposition:2 moment:3 born:3 series:2 score:2 zij:1 att:5 jimenez:2 genetic:1 document:1 past:1 existing:3 recovered:3 dx:2 readily:1 devin:1 partition:1 informative:1 designed:1 bart:2 infant:2 generative:5 intelligence:... |
6,881 | 7,224 | Learning Identifiable Gaussian Bayesian Networks in
Polynomial Time and Sample Complexity
Asish Ghoshal and Jean Honorio
Department of Computer Science, Purdue University, West Lafayette, IN - 47906
{aghoshal, jhonorio}@purdue.edu
Abstract
Learning the directed acyclic graph (DAG) structure of a Bayesian network from ... | 7224 |@word determinant:1 briefly:1 version:1 polynomial:6 seems:1 norm:4 stronger:1 hyv:1 simulation:1 dominique:1 bn:1 covariance:33 hsieh:2 citeseer:1 liu:2 series:2 score:8 selecting:2 daniel:1 denoting:1 outperforms:1 existing:2 current:1 luo:1 si:35 written:1 additive:3 remove:2 drop:1 update:2 v:2 intelligence:6... |
6,882 | 7,225 | Gradient Episodic Memory for Continual Learning
David Lopez-Paz and Marc?Aurelio Ranzato
Facebook Artificial Intelligence Research
{dlp,ranzato}@fb.com
Abstract
One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better
un... | 7225 |@word multitask:3 cu:3 version:1 eliminating:1 compression:1 norm:1 hippocampus:1 sgd:1 shot:12 necessity:1 contains:2 hoiem:2 past:7 outperforms:2 current:4 com:3 contextual:1 comparing:1 realistic:1 partition:1 update:7 alone:1 intelligence:3 ruvolo:2 bwt:13 pascanu:2 revisited:1 location:1 toronto:1 zhang:1 fi... |
6,883 | 7,226 | Effective Parallelisation for Machine Learning
Michael Kamp
University of Bonn
and Fraunhofer IAIS
kamp@cs.uni-bonn.de
Olana Missura
Google Inc.
olanam@google.com
Mario Boley
Max Planck Institute for Informatics
and Saarland University
mboley@mpi-inf.mpg.de
Thomas G?artner
University of Nottingham
thomas.gaertner@not... | 7226 |@word arabic:1 repository:2 version:1 sri:3 polynomial:14 stronger:2 hampson:1 nd:2 dekel:1 mri:1 open:5 prognostic:1 confirms:2 tr:2 nystr:1 harder:1 ld:9 reduction:3 inefficiency:1 liu:1 lichman:1 chervonenkis:3 woodruff:1 daniel:1 franklin:1 dubourg:1 outperforms:2 bradley:1 current:1 com:1 comparing:1 manuel:... |
6,884 | 7,227 | Semisupervised Clustering, AND-Queries and Locally
Encodable Source Coding
Arya Mazumdar
College of Information & Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
arya@cs.umass.edu
Soumyabrata Pal
College of Information & Computer Sciences
University of Massachusetts Amherst
Amherst, MA 01003
s... | 7227 |@word version:5 compression:9 proportion:3 nd:8 c0:2 d2:2 p0:5 asks:1 liu:1 contains:2 uma:2 karger:2 document:1 interestingly:1 franklin:1 outperforms:1 recovered:3 com:2 dx:1 must:10 written:1 john:2 designed:1 plot:2 update:1 v:2 intelligence:1 selected:3 beginning:1 ith:3 vanishing:1 pvldb:3 provides:2 node:7... |
6,885 | 7,228 | Clustering Stable Instances of Euclidean k-means
Abhratanu Dutta?
Northwestern University
adutta@u.northwestern.edu
Aravindan Vijayaraghavan?
Northwestern University
aravindv@northwestern.edu
Alex Wang?
Carnegie Mellon University
alexwang@u.northwestern.edu
Abstract
The Euclidean k-means problem is arguably the mos... | 7228 |@word trial:2 repository:1 version:3 polynomial:8 stronger:2 seems:2 norm:4 nd:1 open:1 hu:1 d2:1 sheffet:3 covariance:1 p0:3 incurs:1 solid:1 series:1 contains:1 ka:2 blank:1 si:16 assigning:1 reminiscent:1 slanted:1 must:2 sergei:1 mst:1 additive:32 realistic:2 john:1 enables:1 remove:2 seeding:1 aps:14 half:6 ... |
6,886 | 7,229 | Good Semi-supervised Learning
That Requires a Bad GAN
Zihang Dai?, Zhilin Yang?, Fan Yang, William W. Cohen, Ruslan Salakhutdinov
School of Computer Science
Carnegie Melon University
dzihang,zhiliny,fanyang1,wcohen,rsalakhu@cs.cmu.edu
Abstract
Semi-supervised learning methods based on generative adversarial networks
... | 7229 |@word mild:2 exploitation:1 middle:1 seems:2 norm:1 open:1 hu:1 bachman:1 pg:25 sgd:1 ld:11 moment:2 initial:1 contains:2 jimenez:1 ours:2 document:1 current:1 com:1 comparing:1 amjad:1 scatter:1 diederik:2 written:1 john:1 realistic:5 ronan:1 informative:2 analytic:1 treating:2 update:1 discrimination:1 generati... |
6,887 | 723 | The Power of Amnesia
Dana Ron
Yoram Singer
Naftali Tishby
Institute of Computer Science and
Center for Neural Computation
Hebrew University, Jerusalem 91904, Israel
Abstract
We propose a learning algorithm for a variable memory length
Markov process. Human communication, whether given as text,
handwriting, or speech,... | 723 |@word polynomial:2 seems:1 tat:1 q1:2 pick:1 automat:1 cgc:1 initial:1 att:1 prefix:8 langdon:1 err:1 current:1 blank:2 si:2 yet:7 written:1 must:7 predetermined:1 remove:1 ti7:1 xex:1 grass:3 stationary:2 leaf:9 short:5 gtg:1 equi:1 node:21 ron:5 mathematical:1 along:1 dn:1 gtt:1 ucsc:2 amnesia:5 descendant:1 con... |
6,888 | 7,230 | On Blackbox Backpropagation and Jacobian Sensing
Vikas Sindhwani
Google Brain
New York, NY 10011
sindhwani@google.com
Krzysztof Choromanski
Google Brain
New York, NY 10011
kchoro@google.com
Abstract
From a small number of calls to a given ?blackbox" on random input perturbations,
we show how to efficiently recover i... | 7230 |@word cnn:1 middle:2 version:1 compression:1 norm:5 polynomial:1 linearized:1 sgd:1 recursively:1 reduction:1 wrapper:1 series:1 lqr:1 rightmost:1 com:2 si:1 chu:1 conforming:1 john:1 subsequent:1 numerical:1 j1:1 cheap:1 analytic:1 designed:1 greedy:1 selected:1 utterly:1 ith:1 core:3 colored:1 mental:1 characte... |
6,889 | 7,231 | Protein Interface Prediction using Graph
Convolutional Networks
Alex Fout?
Department of Computer Science
Colorado State University
Fort Collins, CO 80525
fout@colostate.edu
Jonathon Byrd?
Department of Computer Science
Colorado State University
Fort Collins, CO 80525
jonbyrd@colostate.edu
Basir Shariat?
Department o... | 7231 |@word cnn:1 version:7 propagate:1 bn:2 kutzkov:1 thereby:2 necessity:1 contains:3 score:4 existing:3 steiner:1 current:1 comparing:1 activation:8 assigning:1 attracted:1 gpu:1 devin:1 subsequent:1 additive:1 partition:1 shape:5 designed:1 update:2 v:1 alone:1 isard:1 selected:1 fewer:2 amir:1 plane:1 tertiary:1 p... |
6,890 | 7,232 | Solid Harmonic Wavelet Scattering: Predicting
Quantum Molecular Energy from Invariant
Descriptors of 3D Electronic Densities
Michael Eickenberg
Department of computer science
Ecole normale sup?rieure
PSL Research University, 75005 Paris, France
michael.eickenberg@nsup.org
Georgios Exarchakis
Department of computer sc... | 7232 |@word deformed:1 h:1 katja:4 kondor:1 polynomial:2 norm:1 open:1 azimuthal:2 simulation:1 covariance:2 incurs:1 solid:37 accommodate:1 carry:1 ecole:3 denoting:1 existing:2 imaginary:1 comparing:1 diederik:1 must:1 reminiscent:1 refines:1 numerical:5 j1:17 christian:1 drop:2 bart:2 discrimination:1 plane:1 core:2... |
6,891 | 7,233 | Towards Generalization and Simplicity
in Continuous Control
Aravind Rajeswaran?
Kendall Lowrey?
Emanuel Todorov
Sham Kakade
University of Washington Seattle
{ aravraj, klowrey, todorov, sham } @ cs.washington.edu
Abstract
This work shows that policies with simple linear and RBF parameterizations can
be trained to ... | 7233 |@word version:4 open:2 termination:5 pieter:2 simulation:4 tried:2 seek:1 r:1 contactinvariant:1 harder:2 reduction:1 initial:14 configuration:2 exclusively:1 score:4 pt0:1 rkhs:1 current:1 com:1 surprising:2 cad:1 activation:2 yet:2 si:1 must:4 john:2 ronald:1 realistic:1 shape:1 pertinent:2 motor:4 remove:1 des... |
6,892 | 7,234 | Random Projection Filter Bank for Time Series Data
Amir-massoud Farahmand
Mitsubishi Electric Research Laboratories (MERL)
Cambridge, MA, USA
farahmand@merl.com
Sepideh Pourazarm
Mitsubishi Electric Research Laboratories (MERL)
Cambridge, MA, USA
sepid@bu.edu
Daniel Nikovski
Mitsubishi Electric Research Laboratories (... | 7234 |@word briefly:1 version:2 polynomial:3 norm:1 prognostic:1 nd:1 mitsubishi:3 p0:1 pick:3 mention:1 cius:2 series:56 selecting:2 liquid:1 daniel:2 denoting:1 rkhs:4 past:6 current:2 com:2 z2:1 scovel:1 yet:1 gpu:1 john:1 ronald:1 distant:1 cheap:1 bart:1 stationary:3 intelligence:1 selected:4 device:1 amir:3 short... |
6,893 | 7,235 | Filtering Variational Objectives
Chris J. Maddison1,3,* , Dieterich Lawson,2,* George Tucker2,*
Nicolas Heess1 , Mohammad Norouzi2 , Andriy Mnih1 , Arnaud Doucet3 , Yee Whye Teh1
1
DeepMind, 2 Google Brain, 3 University of Oxford
{cmaddis, dieterichl, gjt}@google.com
Abstract
When used as a surrogate objective for m... | 7235 |@word mild:1 briefly:1 middle:1 proportion:1 simulation:2 fifteen:1 solid:2 moment:3 reduction:1 series:1 contains:1 jimenez:2 denoting:1 current:3 com:1 comparing:3 yet:1 diederik:3 john:1 devin:1 christian:1 treating:3 drop:1 update:2 polyphonic:7 resampling:37 plot:1 generative:6 greedy:2 ivo:1 concat:1 parame... |
6,894 | 7,236 | On Frank-Wolfe and Equilibrium Computation
Jacob Abernethy
Georgia Institute of Technology
prof@gatech.edu
Jun-Kun Wang
Georgia Institute of Technology
jimwang@gatech.edu
Abstract
We consider the Frank-Wolfe (FW) method for constrained convex optimization,
and we show that this classical technique can be interpreted... | 7236 |@word norm:7 instrumental:1 stronger:2 closure:3 crucially:2 jacob:3 decomposition:1 paid:1 minus:1 biconjugate:1 minding:1 substitution:1 celebrated:1 series:1 daniel:1 existing:4 current:3 surprising:1 luo:1 yet:5 intriguing:1 must:3 john:1 zaid:1 update:5 juditsky:1 selected:1 amir:1 inspection:2 vanishing:3 c... |
6,895 | 7,237 | Modulating early visual processing by language
Harm de Vries?
Florian Strub?
J?r?mie Mary?
University of Montreal
mail@harmdevries.com
Univ. Lille, CNRS, Centrale Lille,
Inria, UMR 9189 CRIStAL
florian.strub@inria.fr
Univ. Lille, CNRS, Centrale Lille,
Inria, UMR 9189 CRIStAL
jeremie.mary@univ-lille3.fr
Hugo Laro... | 7237 |@word briefly:2 norm:4 open:3 cleanly:1 confirms:1 bn:14 decomposition:3 initial:2 contains:2 series:1 exclusively:1 ours:1 interestingly:4 outperforms:3 existing:2 current:4 com:5 cad:1 activation:6 gmail:1 si:1 must:1 gpu:1 refines:1 numerical:1 concatenate:3 enables:2 christian:1 designed:1 drop:1 update:1 v:2... |
6,896 | 7,238 | Learning Mixture of Gaussians with Streaming Data
Aditi Raghunathan
Stanford University
aditir@stanford.edu
Prateek Jain
Microsoft Research, India
prajain@microsoft.com
Ravishankar Krishnaswamy
Microsoft Research, India
rakri@microsoft.com
Abstract
In this paper, we study the problem of learning a mixture of Gaussi... | 7238 |@word version:17 norm:3 duda:1 nd:1 git:16 simplifying:1 covariance:1 decomposition:2 incurs:2 moment:1 reduction:1 celebrated:1 initial:10 daniel:3 ours:1 current:5 com:2 yet:1 john:2 distant:1 seeding:1 remove:1 designed:2 update:26 n0:25 drop:1 generative:2 kyk:1 ith:1 farther:1 iterates:6 provides:1 firstly:1... |
6,897 | 7,239 | Practical Hash Functions for Similarity Estimation
and Dimensionality Reduction
S?ren Dahlgaard
University of Copenhagen / SupWiz
s.dahlgaard@supwiz.com
Mathias B?k Tejs Knudsen
University of Copenhagen / SupWiz
m.knudsen@supwiz.com
Mikkel Thorup
University of Copenhagen
mthorup@di.ku.dk
Abstract
Hashing is a basic... | 7239 |@word multitask:1 version:2 briefly:2 polynomial:2 norm:3 compression:1 nd:1 confirms:1 crucially:1 pick:1 thereby:1 harder:1 moment:1 reduction:6 dff:1 tist:1 document:3 outperforms:1 hearn:1 torben:1 com:6 comparing:1 whp:1 crawling:1 bd:1 john:1 additive:1 partition:2 visible:1 confirming:2 razenshteyn:4 chris... |
6,898 | 724 | Credit Assignment through Time:
Alternatives to Backpropagation
Yoshua Bengio *
Dept. Informatique et
Recherche Operationnelle
Universite de Montreal
Montreal, Qc H3C-3J7
Paolo Frasconi
Dip. di Sistemi e Informatica
Universita di Firenze
50139 Firenze (Italy)
Abstract
Learning to recognize or predict sequences using... | 724 |@word trial:3 determinant:2 open:1 propagate:2 jacob:2 b39:1 carry:1 initial:2 err:4 current:2 activation:2 assigning:2 additive:1 j1:1 cheap:1 update:1 vanishing:1 short:1 recherche:1 quantized:1 node:1 mathematical:3 along:1 constructed:1 prove:1 consists:1 inside:1 introduce:1 operationnelle:1 theoretically:1 i... |
6,899 | 7,240 | GANs Trained by a Two Time-Scale Update Rule
Converge to a Local Nash Equilibrium
Martin Heusel
Hubert Ramsauer
Thomas Unterthiner
Bernhard Nessler
Sepp Hochreiter
LIT AI Lab & Institute of Bioinformatics,
Johannes Kepler University Linz
A-4040 Linz, Austria
{mhe,ramsauer,unterthiner,nessler,hochreit}@bioinf.jku.a... | 7240 |@word mild:1 cnn:2 version:2 pw:5 polynomial:4 middle:3 norm:5 open:1 d2:1 km:1 prasad:4 propagate:1 decomposition:3 recapitulate:1 covariance:4 jacob:2 tr:1 solid:3 ld:3 carry:2 moment:15 liu:1 score:9 jku:2 past:1 outperforms:4 current:1 activation:2 dx:2 must:4 fn:2 realistic:3 additive:2 blur:2 hofmann:1 enab... |
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