index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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5,900 | Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial David I. Inouye Pradeep Ravikumar Inderjit S. Dhillon Department of Computer Science University of Texas at Austin {dinouye,pradeepr,inderjit}@cs.utexas.edu Abstract We propose a novel distribution that generalizes the Multinomial distri... | 2015 | 379 |
5,901 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren∗ Kaiming He Ross Girshick Jian Sun Microsoft Research {v-shren, kahe, rbg, jiansun}@microsoft.com Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object... | 2015 | 38 |
5,902 | Stochastic Expectation Propagation Yingzhen Li University of Cambridge Cambridge, CB2 1PZ, UK yl494@cam.ac.uk Jos´e Miguel Hern´andez-Lobato Harvard University Cambridge, MA 02138 USA jmh@seas.harvard.edu Richard E. Turner University of Cambridge Cambridge, CB2 1PZ, UK ret26@cam.ac.uk Abstract... | 2015 | 380 |
5,903 | Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar Arindam Banerjee Department of Computer Science & Engineering University of Minnesota, Twin Cities {sivakuma,banerjee}@cs.umn.edu Pradeep Ravikumar Department of Computer Scienc... | 2015 | 381 |
5,904 | Fast Randomized Kernel Ridge Regression with Statistical Guarantees∗ Ahmed El Alaoui † Michael W. Mahoney ‡ † Electrical Engineering and Computer Sciences ‡ Statistics and International Computer Science Institute University of California, Berkeley, Berkeley, CA 94720. {elalaoui@eecs,mmahoney@stat}.berkele... | 2015 | 382 |
5,905 | Skip-Thought Vectors Ryan Kiros 1, Yukun Zhu 1, Ruslan Salakhutdinov 1,2, Richard S. Zemel 1,2 Antonio Torralba 3, Raquel Urtasun 1, Sanja Fidler 1 University of Toronto 1 Canadian Institute for Advanced Research 2 Massachusetts Institute of Technology 3 Abstract We describe an approach for unsupervised l... | 2015 | 383 |
5,906 | Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao Hsiang-Fu Yu Pradeep Ravikumar Inderjit S. Dhillon {nikhilr, rofuyu, paradeepr, inderjit}@cs.utexas.edu Department of Computer Science University of Texas at Austin Abstract Low rank matrix completion plays a... | 2015 | 384 |
5,907 | Gaussian Process Random Fields David A. Moore and Stuart J. Russell Computer Science Division University of California, Berkeley Berkeley, CA 94709 {dmoore, russell}@cs.berkeley.edu Abstract Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their com... | 2015 | 385 |
5,908 | Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Seunghoon Hong∗Hyeonwoo Noh∗ Bohyung Han Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea {maga33,hyeonwoonoh ,bhhan}@postech.ac.kr Abstract We propose a novel deep neural network architecture for semi-supervised sema... | 2015 | 386 |
5,909 | Discrete R´enyi Classifiers Meisam Razaviyayn∗ meisamr@stanford.edu Farzan Farnia∗ farnia@stanford.edu David Tse∗ dntse@stanford.edu Abstract Consider the binary classification problem of predicting a target variable Y from a discrete feature vector X = (X1, . . . , Xd). When the probability distributio... | 2015 | 387 |
5,910 | Preconditioned Spectral Descent for Deep Learning David E. Carlson,1 Edo Collins,2 Ya-Ping Hsieh,2 Lawrence Carin,3 Volkan Cevher2 1 Department of Statistics, Columbia University 2 Laboratory for Information and Inference Systems (LIONS), EPFL 3 Department of Electrical and Computer Engineering, Duke University... | 2015 | 388 |
5,911 | Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene UC Berkeley walid@eecs.berkeley.edu Alexandre M. Bayen UC Berkeley bayen@berkeley.edu Peter L. Bartlett UC Berkeley and QUT bartlett@berkeley.edu Abstract We study accelerated mirror descent dynamics in continuous and disc... | 2015 | 389 |
5,912 | Efficient Non-greedy Optimization of Decision Trees Mohammad Norouzi1∗ Maxwell D. Collins2 ∗ Matthew Johnson3 David J. Fleet4 Pushmeet Kohli5 1,4 Department of Computer Science, University of Toronto 2 Department of Computer Science, University of Wisconsin-Madison 3,5 Microsoft Research Abstract Dec... | 2015 | 39 |
5,913 | Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li Zhouchen Lin B Key Lab. of Machine Perception (MOE), School of EECS, Peking University, P. R. China Cooperative Medianet Innovation Center, Shanghai Jiaotong University, P. R. China lihuanss@pku.edu.cn zlin@pku.edu.cn Abstract Non... | 2015 | 390 |
5,914 | Monotone k-Submodular Function Maximization with Size Constraints Naoto Ohsaka The University of Tokyo ohsaka@is.s.u-tokyo.ac.jp Yuichi Yoshida National Institute of Informatics, and Preferred Infrastructure, Inc. yyoshida@nii.ac.jp Abstract A k-submodular function is a generalization of a submodula... | 2015 | 391 |
5,915 | Spherical Random Features for Polynomial Kernels Jeffrey Pennington Felix X. Yu Sanjiv Kumar Google Research {jpennin, felixyu, sanjivk}@google.com Abstract Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of... | 2015 | 392 |
5,916 | A Dual-Augmented Block Minimization Framework for Learning with Limited Memory Ian E.H. Yen ∗ Shan-Wei Lin † Shou-De Lin † ∗University of Texas at Austin † National Taiwan University ∗ianyen@cs.utexas.edu {r03922067,sdlin}@csie.ntu.edu.tw Abstract In past few years, several techniques have been prop... | 2015 | 393 |
5,917 | Convolutional Networks on Graphs for Learning Molecular Fingerprints David Duvenaud†, Dougal Maclaurin†, Jorge Aguilera-Iparraguirre Rafael G´omez-Bombarelli, Timothy Hirzel, Al´an Aspuru-Guzik, Ryan P. Adams Harvard University Abstract We introduce a convolutional neural network that operates directly on g... | 2015 | 394 |
5,918 | Decomposition Bounds for Marginal MAP Wei Ping∗ Qiang Liu† Alexander Ihler∗ ∗Computer Science, UC Irvine †Computer Science, Dartmouth College {wping,ihler}@ics.uci.edu qliu@cs.dartmouth.edu Abstract Marginal MAP inference involves making MAP predictions in systems defined with latent variables or mis... | 2015 | 395 |
5,919 | The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions Sebastian Bitzer1 sebastian.bitzer@tu-dresden.de Stefan J. Kiebel1 stefan.kiebel@tu-dresden.de 1Department of Psychology, Technische Universit¨at Dresden, 01062 Dresden, Germany Abstract In simple perceptual decisions th... | 2015 | 396 |
5,920 | Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering Haoyuan Gao1 Junhua Mao2 Jie Zhou1 Zhiheng Huang1 Lei Wang1 Wei Xu1 1Baidu Research 2University of California, Los Angeles gaohaoyuan@baidu.com, mjhustc@ucla.edu, {zhoujie01,huangzhiheng,wanglei22,wei.xu}@bai... | 2015 | 397 |
5,921 | The Pseudo-Dimension of Near-Optimal Auctions Jamie Morgenstern⇤ Computer and Information Science University of Pennsylvania Philadelphia, PA jamiemor@cis.upenn.edu Tim Roughgarden Stanford University Palo Alto, CA tim@cs.stanford.edu Abstract This paper develops a general approach, rooted in stat... | 2015 | 398 |
5,922 | Fast Second-Order Stochastic Backpropagation for Variational Inference Kai Fan Duke University kai.fan@stat.duke.edu Ziteng Wang∗ HKUST† wangzt2012@gmail.com Jeffrey Beck Duke University jeff.beck@duke.edu James T. Kwok HKUST jamesk@cse.ust.hk Katherine Heller Duke University kheller@gma... | 2015 | 399 |
5,923 | A Fast, Universal Algorithm to Learn Parametric Nonlinear Embeddings Miguel ´A. Carreira-Perpi˜n´an EECS, University of California, Merced http://eecs.ucmerced.edu Max Vladymyrov UC Merced and Yahoo Labs maxv@yahoo-inc.com Abstract Nonlinear embedding algorithms such as stochastic neighbor embedding d... | 2015 | 4 |
5,924 | Learning with Incremental Iterative Regularization Lorenzo Rosasco DIBRIS, Univ. Genova, ITALY LCSL, IIT & MIT, USA lrosasco@mit.edu Silvia Villa LCSL, IIT & MIT, USA Silvia.Villa@iit.it Abstract Within a statistical learning setting, we propose and study an iterative regularization algorithm for leas... | 2015 | 40 |
5,925 | Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions Yuya Yoshikawa∗ Nara Institute of Science and Technology Nara, 630-0192, Japan yoshikawa.yuya.yl9@is.naist.jp Tomoharu Iwata NTT Communication Science Laboratories Kyoto, 619-0237, Japan iwata.tomoharu@lab.ntt.co... | 2015 | 400 |
5,926 | Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring Gunwoong Park Department of Statistics University of Wisconsin-Madison Madison, WI 53706 parkg@stat.wisc.edu Garvesh Raskutti Department of Statistics Department of Computer Science Wisconsin Institute for Discovery, Optimizatio... | 2015 | 401 |
5,927 | Local Causal Discovery of Direct Causes and Effects Tian Gao Qiang Ji Department of ECSE Rensselaer Polytechnic Institute, Troy, NY 12180 {gaot, jiq}@rpi.edu Abstract We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art causa... | 2015 | 402 |
5,928 | Recognizing retinal ganglion cells in the dark Emile Richard Stanford University emileric@stanford.edu Georges Goetz Stanford University ggoetz@stanford.edu E.J. Chichilnisky Stanford University ej@stanford.edu Abstract Many neural circuits are composed of numerous distinct cell types that perform... | 2015 | 403 |
5,929 | Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke NICTA, Australian National University justin.domke@nicta.com.au Abstract Inference is typically intractable in high-treewidth undirected graphical models, making maximum likelihood learning a challenge. One wa... | 2015 | 41 |
5,930 | Sampling from Probabilistic Submodular Models Alkis Gotovos ETH Zurich alkisg@inf.ethz.ch S. Hamed Hassani ETH Zurich hamed@inf.ethz.ch Andreas Krause ETH Zurich krausea@ethz.ch Abstract Submodular and supermodular functions have found wide applicability in machine learning, capturing notions such... | 2015 | 42 |
5,931 | A class of network models recoverable by spectral clustering Yali Wan Department of Statistics University of Washington Seattle, WA 98195-4322, USA yaliwan@washington.edu Marina Meil˘a Department of Statistics University of Washington Seattle, WA 98195-4322, USA mmp@stat.washington.edu Abstract ... | 2015 | 43 |
5,932 | Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang IBM T.J. Watson Research Center eunhyang@us.ibm.com Aur´elie C. Lozano IBM T.J. Watson Research Center aclozano@us.ibm.com Pradeep Ravikumar University of Texas at Austin pradeepr@cs.utexas.edu Abstract We propose a... | 2015 | 44 |
5,933 | Expressing an Image Stream with a Sequence of Natural Sentences Cesc Chunseong Park Gunhee Kim Seoul National University, Seoul, Korea {park.chunseong,gunhee}@snu.ac.kr https://github.com/cesc-park/CRCN Abstract We propose an approach for retrieving a sequence of natural sentences for an image stream.... | 2015 | 45 |
5,934 | Learning spatiotemporal trajectories from manifold-valued longitudinal data Jean-Baptiste Schiratti2,1, St´ephanie Allassonni`ere2, Olivier Colliot1, Stanley Durrleman1 1 ARAMIS Lab, INRIA Paris, Inserm U1127, CNRS UMR 7225, Sorbonne Universit´es, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moel... | 2015 | 46 |
5,935 | Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li Adarsh Prasad Department of Computer Science, UT Austin {lty,adarsh,pradeepr}@cs.utexas.edu Pradeep Ravikumar Abstract We consider the problem of binary classification when the covariates conditioned on the each of the... | 2015 | 47 |
5,936 | Adaptive Online Learning Dylan J. Foster ∗ Cornell University Alexander Rakhlin † University of Pennsylvania Karthik Sridharan ∗ Cornell University Abstract We propose a general framework for studying adaptive regret bounds in the online learning setting, subsuming model selection and data-dependent b... | 2015 | 48 |
5,937 | Robust Regression via Hard Thresholding Kush Bhatia†, Prateek Jain†, and Purushottam Kar‡∗ †Microsoft Research, India ‡Indian Institute of Technology Kanpur, India {t-kushb,prajain}@microsoft.com, purushot@cse.iitk.ac.in Abstract We study the problem of Robust Least Squares Regression (RLSR) where several ... | 2015 | 49 |
5,938 | Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin Tsinghua University Beijing, China lintian06@gmail.com Jian Li Tsinghua University Beijing, China lapordge@gmail.com Wei Chen Microsoft Research Beijing, China weic@microsoft.com Abstract The greedy algorithm is extensiv... | 2015 | 5 |
5,939 | b-bit Marginal Regression Martin Slawski Department of Statistics and Biostatistics Department of Computer Science Rutgers University martin.slawski@rutgers.edu Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University pingli@stat.rutgers.edu Abstract W... | 2015 | 50 |
5,940 | Spectral Norm Regularization of Orthonormal Representations for Graph Transduction Rakesh Shivanna Google Inc. Mountain View, CA, USA rakeshshivanna@google.com Bibaswan Chatterjee Dept. of Computer Science & Automation Indian Institute of Science, Bangalore bibaswan.chatterjee@csa.iisc.ernet.in Rama... | 2015 | 51 |
5,941 | Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco Massachusetts Institute of Technology, EECS Cambridge, MA 02139, USA cnmusco@mit.edu Christopher Musco Massachusetts Institute of Technology, EECS Cambridge, MA 02139, USA cpmusco@mit.edu A... | 2015 | 52 |
5,942 | Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath EECS, MIT {jayadev, costis, g}@mit.edu Abstract Given samples from an unknown discrete distribution p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far fro... | 2015 | 53 |
5,943 | Combinatorial Cascading Bandits Branislav Kveton Adobe Research San Jose, CA kveton@adobe.com Zheng Wen Yahoo Labs Sunnyvale, CA zhengwen@yahoo-inc.com Azin Ashkan Technicolor Research Los Altos, CA azin.ashkan@technicolor.com Csaba Szepesv´ari Department of Computing Science University of... | 2015 | 54 |
5,944 | Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process Ye Wang Department of Statistics Duke University Durham, NC, USA, 27705 eric.ye.wang@duke.edu David Dunson Department of Statistics Duke University Durham, NC, USA, 27705 dunson@stat.duke.edu Abstract Learni... | 2015 | 55 |
5,945 | Training Very Deep Networks Rupesh Kumar Srivastava Klaus Greff J¨urgen Schmidhuber The Swiss AI Lab IDSIA / USI / SUPSI {rupesh, klaus, juergen}@idsia.ch Abstract Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success. However, training becomes more ... | 2015 | 56 |
5,946 | Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun MSR, Cambridge seyoung.yun@inria.fr Marc Lelarge ∗ Inria & ENS marc.lelarge@ens.fr Alexandre Proutiere † KTH, EE School / ACL alepro@kth.se Abstract In this paper, we revisit the problem of constructing a near-optimal rank k appr... | 2015 | 57 |
5,947 | Character-level Convolutional Networks for Text Classification∗ Xiang Zhang Junbo Zhao Yann LeCun Courant Institute of Mathematical Sciences, New York University 719 Broadway, 12th Floor, New York, NY 10003 {xiang, junbo.zhao, yann}@cs.nyu.edu Abstract This article offers an empirical exploration on th... | 2015 | 58 |
5,948 | Interactive Control of Diverse Complex Characters with Neural Networks Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel Todorov Department of Computer Science, University of Washington {mordatch,lowrey,galen,zoran,todorov}@cs.washington.edu Abstract We present a method for training recurr... | 2015 | 59 |
5,949 | SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun Virginia Tech sunqing@vt.edu Dhruv Batra Virginia Tech https://mlp.ece.vt.edu/ Abstract This paper formulates the search for a set of bounding boxes (as needed in object proposal generation) as a monotone submodular maxi... | 2015 | 6 |
5,950 | Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin Facebook AI Research 770 Broadway, New York, USA. ajoulin@fb.com Tomas Mikolov Facebook AI Research 770 Broadway, New York, USA. tmikolov@fb.com Abstract Despite the recent achievements in machine learning, we are sti... | 2015 | 60 |
5,951 | Grammar as a Foreign Language Oriol Vinyals∗ Google vinyals@google.com Lukasz Kaiser∗ Google lukaszkaiser@google.com Terry Koo Google terrykoo@google.com Slav Petrov Google slav@google.com Ilya Sutskever Google ilyasu@google.com Geoffrey Hinton Google geoffhinton@google.com Abstrac... | 2015 | 61 |
5,952 | Practical and Optimal LSH for Angular Distance Alexandr Andoni∗ Columbia University Piotr Indyk MIT Thijs Laarhoven TU Eindhoven Ilya Razenshteyn MIT Ludwig Schmidt MIT Abstract We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate... | 2015 | 62 |
5,953 | GP Kernels for Cross-Spectrum Analysis 1Kyle Ulrich, 3David E. Carlson, 2Kafui Dzirasa, 1Lawrence Carin 1Department of Electrical and Computer Engineering, Duke University 2Department of Psychiatry and Behavioral Sciences, Duke University 3Department of Statistics, Columbia University {kyle.ulrich, kafui.dzir... | 2015 | 63 |
5,954 | A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam Dept. of Computer Science Johns Hopkins University Baltimore, MD 21218 pschulam@jhu.edu Suchi Saria Dept. of Computer Science Johns Hopkins University Baltimore, MD 21218 ssar... | 2015 | 64 |
5,955 | Local Smoothness in Variance Reduced Optimization Daniel Vainsencher, Han Liu Tong Zhang Dept. of Operations Research & Financial Engineering Dept. of Statistics Princeton University Rutgers University Princeton, NJ 08544 Piscataway, NJ, 08854 {daniel.vainsencher,han.liu}@princeton.edu tzhang@stat.r... | 2015 | 65 |
5,956 | Unlocking neural population non-stationarity using a hierarchical dynamics model Mijung Park1, Gergo Bohner1, Jakob H. Macke2 1 Gatsby Computational Neuroscience Unit, University College London 2 Research Center caesar, an associate of the Max Planck Society, Bonn Max Planck Institute for Biological Cyberneti... | 2015 | 66 |
5,957 | Pointer Networks Oriol Vinyals∗ Google Brain Meire Fortunato∗ Department of Mathematics, UC Berkeley Navdeep Jaitly Google Brain Abstract We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions ... | 2015 | 67 |
5,958 | Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre EPFL lucas.maystre@epfl.ch Matthias Grossglauser EPFL matthias.grossglauser@epfl.ch Abstract We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed a... | 2015 | 68 |
5,959 | Learning Bayesian Networks with Thousands of Variables Mauro Scanagatta IDSIA∗, SUPSI†, USI‡ Lugano, Switzerland mauro@idsia.ch Cassio P. de Campos Queen’s University Belfast Northern Ireland, UK c.decampos@qub.ac.uk Giorgio Corani IDSIA∗, SUPSI†, USI‡ Lugano, Switzerland giorgio@idsia.ch Ma... | 2015 | 69 |
5,960 | Robust Portfolio Optimization Huitong Qiu Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 hqiu7@jhu.edu Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 fhan@jhu.edu Han Liu Department of Operations Research and Financial Engineering ... | 2015 | 7 |
5,961 | Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas∗ University of Edinburgh Moritz Hardt Google Research Ludwig Schmidt MIT Abstract We investigate the problem of learning an unknown probability distribution over a discrete population from random samples. Our goa... | 2015 | 70 |
5,962 | Generative Image Modeling Using Spatial LSTMs Lucas Theis University of T¨ubingen 72076 T¨ubingen, Germany lucas@bethgelab.org Matthias Bethge University of T¨ubingen 72076 T¨ubingen, Germany matthias@bethgelab.org Abstract Modeling the distribution of natural images is challenging, partly because o... | 2015 | 71 |
5,963 | Sparse PCA via Bipartite Matchings Megasthenis Asteris The University of Texas at Austin megas@utexas.edu Dimitris Papailiopoulos University of California, Berkeley dimitrisp@berkeley.edu Anastasios Kyrillidis The University of Texas at Austin anastasios@utexas.edu Alexandros G. Dimakis The Univer... | 2015 | 72 |
5,964 | Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Mithun Chakraborty, Sanmay Das Department of Computer Science and Engineering Washington University in St. Louis St. Louis, MO 63130 {mithunchakraborty,sanmay}@wustl.edu Abstract A market scoring rule (MSR) – a popular tool for designing a... | 2015 | 73 |
5,965 | Lifted Inference Rules with Constraints Happy Mittal, Anuj Mahajan Dept. of Comp. Sci. & Engg. I.I.T. Delhi, Hauz Khas New Delhi, 110016, India happy.mittal@cse.iitd.ac.in, anujmahajan.iitd@gmail.com Vibhav Gogate Dept. of Comp. Sci. Univ. of Texas Dallas Richardson, TX 75080, USA vgogate@hlt.utda... | 2015 | 74 |
5,966 | LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements Christos Thrampoulidis, Department of Electrical Engineering Caltech cthrampo@caltech.edu Ehsan Abbasi Department of Electrical Engineering Caltech eabbasi@caltech.edu Babak Hassibi Department of Electrical Engineerin... | 2015 | 75 |
5,967 | Natural Neural Networks Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, Koray Kavukcuoglu {gdesjardins,simonyan,razp,korayk}@google.com Google DeepMind, London Abstract We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representatio... | 2015 | 76 |
5,968 | Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William Stephenson, and Erik B. Sudderth Department of Computer Science, Brown University, Providence, RI 02912 mhughes@cs.brown.edu, wtstephe@gmail.com, sudderth@cs.brown.edu Abstract Bayesian nonparametric hi... | 2015 | 77 |
5,969 | Inference for determinantal point processes without spectral knowledge R´emi Bardenet∗ CNRS & CRIStAL UMR 9189, Univ. Lille, France remi.bardenet@gmail.com Michalis K. Titsias∗ Department of Informatics Athens Univ. of Economics and Business, Greece mtitsias@aueb.gr ∗Both authors contributed equally... | 2015 | 78 |
5,970 | A Bayesian Framework for Modeling Confidence in Perceptual Decision Making Koosha Khalvati, Rajesh P. N. Rao Department of Computer Science and Engineering University of Washington Seattle, WA 98195 {koosha, rao}@cs.washington.edu Abstract The degree of confidence in one’s choice or decision is a critical... | 2015 | 79 |
5,971 | Top-k Multiclass SVM Maksim Lapin,1 Matthias Hein2 and Bernt Schiele1 1Max Planck Institute for Informatics, Saarbrücken, Germany 2Saarland University, Saarbrücken, Germany Abstract Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discri... | 2015 | 8 |
5,972 | Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann Machine Learning Department Carnegie Mellon University cdann@cdann.net Emma Brunskill Computer Science Department Carnegie Mellon University ebrun@cs.cmu.edu Abstract Recently, there has been significant progress in u... | 2015 | 80 |
5,973 | Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu University of California at Davis hswu@ucdavis.edu R. Srikant University of Illinois at Urbana-Champaign rsrikant@illinois.edu Xin Liu University of California at Davis liu@cs.ucdavis.edu Chong Jiang Uni... | 2015 | 81 |
5,974 | Latent Bayesian melding for integrating individual and population models Mingjun Zhong, Nigel Goddard, Charles Sutton School of Informatics University of Edinburgh United Kingdom {mzhong,nigel.goddard,csutton}@inf.ed.ac.uk Abstract In many statistical problems, a more coarse-grained model may be suitabl... | 2015 | 82 |
5,975 | Regressive Virtual Metric Learning Micha¨el Perrot, and Amaury Habrard Universit´e de Lyon, Universit´e Jean Monnet de Saint-Etienne, Laboratoire Hubert Curien, CNRS, UMR5516, F-42000, Saint-Etienne, France. {michael.perrot,amaury.habrard}@univ-st-etienne.fr Abstract We are interested in supervised metric l... | 2015 | 83 |
5,976 | Halting in Random Walk Kernels Mahito Sugiyama ISIR, Osaka University, Japan JST, PRESTO mahito@ar.sanken.osaka-u.ac.jp Karsten M. Borgwardt D-BSSE, ETH Z¨urich Basel, Switzerland karsten.borgwardt@bsse.ethz.ch Abstract Random walk kernels measure graph similarity by counting matching walks in two... | 2015 | 84 |
5,977 | Kullback-Leibler Proximal Variational Inference Mohammad Emtiyaz Khan∗ Ecole Polytechnique F´ed´erale de Lausanne Lausanne, Switzerland emtiyaz@gmail.com Pierre Baqu´e∗ Ecole Polytechnique F´ed´erale de Lausanne Lausanne, Switzerland pierre.baque@epfl.ch Franc¸ois Fleuret Idiap Research Institute ... | 2015 | 85 |
5,978 | A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements Qinqing Zheng University of Chicago qinqing@cs.uchicago.edu John Lafferty University of Chicago lafferty@galton.uchicago.edu Abstract We propose a simple, scalable, and fast grad... | 2015 | 86 |
5,979 | On-the-Job Learning with Bayesian Decision Theory Keenon Werling Department of Computer Science Stanford University keenon@cs.stanford.edu Arun Chaganty Department of Computer Science Stanford University chaganty@cs.stanford.edu Percy Liang Department of Computer Science Stanford University plia... | 2015 | 87 |
5,980 | Spatial Transformer Networks Max Jaderberg Karen Simonyan Andrew Zisserman Koray Kavukcuoglu Google DeepMind, London, UK {jaderberg,simonyan,zisserman,korayk}@google.com Abstract Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability... | 2015 | 88 |
5,981 | Precision-Recall-Gain Curves: PR Analysis Done Right Peter A. Flach Intelligent Systems Laboratory University of Bristol, United Kingdom Peter.Flach@bristol.ac.uk Meelis Kull Intelligent Systems Laboratory University of Bristol, United Kingdom Meelis.Kull@bristol.ac.uk Abstract Precision-Recall an... | 2015 | 89 |
5,982 | Less is More: Nystr¨om Computational Regularization Alessandro Rudi† Raffaello Camoriano†‡ Lorenzo Rosasco†◦ †Universit`a degli Studi di Genova - DIBRIS, Via Dodecaneso 35, Genova, Italy ‡Istituto Italiano di Tecnologia - iCub Facility, Via Morego 30, Genova, Italy ◦Massachusetts Institute of Technology and... | 2015 | 9 |
5,983 | Planar Ultrametrics for Image Segmentation Julian Yarkony Experian Data Lab San Diego, CA 92130 julian.yarkony@experian.com Charless C. Fowlkes Department of Computer Science University of California Irvine fowlkes@ics.uci.edu Abstract We study the problem of hierarchical clustering on planar graphs... | 2015 | 90 |
5,984 | Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia†, Himanshu Jain§, Purushottam Kar‡∗, Manik Varma†, and Prateek Jain† †Microsoft Research, India §Indian Institute of Technology Delhi, India ‡Indian Institute of Technology Kanpur, India {t-kushb,prajain,manik}@microsoft.com himansh... | 2015 | 91 |
5,985 | Super-Resolution Off the Grid Qingqing Huang MIT, EECS, LIDS, qqh@mit.edu Sham M. Kakade University of Washington, Department of Statistics, Computer Science & Engineering, sham@cs.washington.edu Abstract Super-resolution is the problem of recovering a superposition of point sources using bandli... | 2015 | 92 |
5,986 | Automatic Variational Inference in Stan Alp Kucukelbir Columbia University alp@cs.columbia.edu Rajesh Ranganath Princeton University rajeshr@cs.princeton.edu Andrew Gelman Columbia University gelman@stat.columbia.edu David M. Blei Columbia University david.blei@columbia.edu Abstract Variatio... | 2015 | 93 |
5,987 | Extending Gossip Algorithms to Distributed Estimation of U-Statistics Igor Colin, Joseph Salmon, St´ephan Cl´emenc¸on LTCI, CNRS, T´el´ecom ParisTech Universit´e Paris-Saclay 75013 Paris, France first.last@telecom-paristech.fr Aur´elien Bellet Magnet Team INRIA Lille - Nord Europe 59650 Villeneuve d... | 2015 | 94 |
5,988 | Model-Based Relative Entropy Stochastic Search Abbas Abdolmaleki1,2,3, Rudolf Lioutikov4, Nuno Lau1, Luis Paulo Reis2,3, Jan Peters4,6, and Gerhard Neumann5 1: IEETA, University of Aveiro, Aveiro, Portugal 2: DSI, University of Minho, Braga, Portugal 3: LIACC, University of Porto, Porto, Portugal 4: IAS, 5:... | 2015 | 95 |
5,989 | Semi-Supervised Learning with Ladder Networks Antti Rasmus and Harri Valpola The Curious AI Company, Finland Mikko Honkala Nokia Labs, Finland Mathias Berglund and Tapani Raiko Aalto University, Finland & The Curious AI Company, Finland Abstract We combine supervised learning with unsupervised learning ... | 2015 | 96 |
5,990 | Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi Department of Complex and Intelligent Systems Future University Hakodate 116-2 Kamedanakano, Hakodate, Hokkaido, 040-8655, Japan ttakashi@fun.ac.jp Takafumi Kanamori Department of Computer Science and Mathemat... | 2015 | 97 |
5,991 | Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan Group For Neural Theory, ENS Paris Rue dUlm, 29, Paris, France ralph.bourdoukan@gmail.com Sophie Deneve Group For Neural Theory, ENS Paris Rue dUlm, 29, Paris, France sophie.deneve@ens.fr Abstract To ... | 2015 | 98 |
5,992 | Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava Technion Haifa, Israel oanava@tx.technion.ac.il Elad Hazan Princeton University New York, USA ehazan@cs.princeton.edu Shie Mannor Technion Haifa, Israel shie@ee.technion.ac.il Abstract The framework of online le... | 2015 | 99 |
5,993 | Eliciting Categorical Data for Optimal Aggregation Chien-Ju Ho Cornell University ch624@cornell.edu Rafael Frongillo CU Boulder raf@colorado.edu Yiling Chen Harvard University yiling@seas.harvard.edu Abstract Models for collecting and aggregating categorical data on crowdsourcing platforms typical... | 2016 | 1 |
5,994 | Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus1, Umut S¸ims¸ekli1, ´Eric Moulines2, Roland Badeau1, Ga¨el Richard1 1: LTCI, CNRS, T´el´ecom ParisTech, Universit´e Paris-Saclay, 75013, Paris, France 2: Centre de Math´ematiques Appliqu´ees, UMR 7641, ´Ecole Polytechnique, France Ab... | 2016 | 10 |
5,995 | Active Learning with Oracle Epiphany Tzu-Kuo Huang ∗ Uber Advanced Technologies Group Pittsburgh, PA 15201 Lihong Li Microsoft Research Redmond, WA 98052 Ara Vartanian University of Wisconsin–Madison Madison, WI 53706 Saleema Amershi Microsoft Research Redmond, WA 98052 Xiaojin Zhu Universit... | 2016 | 100 |
5,996 | Stochastic Optimization for Large-scale Optimal Transport Aude Genevay CEREMADE, Université Paris-Dauphine INRIA – Mokaplan project-team genevay@ceremade.dauphine.fr Marco Cuturi CREST, ENSAE Université Paris-Saclay marco.cuturi@ensae.fr Gabriel Peyré CNRS and DMA, École Normale Supérieure INRIA... | 2016 | 101 |
5,997 | The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM Damek Davis and Madeleine Udell Cornell University {dsd95,mru8}@cornell.edu Brent Edmunds University of California, Los Angeles brent.edmunds@math.ucla.edu Abstract We introduce the Stochastic Asyn... | 2016 | 102 |
5,998 | Coresets for Scalable Bayesian Logistic Regression Jonathan H. Huggins Trevor Campbell Tamara Broderick Computer Science and Artificial Intelligence Laboratory, MIT {jhuggins@, tdjc@, tbroderick@csail.}mit.edu Abstract The use of Bayesian methods in large-scale data settings is attractive because of the ... | 2016 | 103 |
5,999 | Sorting out typicality with the inverse moment matrix SOS polynomial Jean-Bernard Lasserre LAAS-CNRS & IMT Université de Toulouse 31400 Toulouse, France lasserre@laas.fr Edouard Pauwels IRIT & IMT Université Toulouse 3 Paul Sabatier 31400 Toulouse, France edouard.pauwels@irit.fr Abstract We st... | 2016 | 104 |
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