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ORCIDNIPS 2016: Barcelona, Spain
- Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett:
Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 2016 - Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. 1-9 - Yan Yang, Jian Sun, Huibin Li, Zongben Xu:
Deep ADMM-Net for Compressive Sensing MRI. 10-18 - Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. 19-27 - Saurabh Singh, Derek Hoiem, David A. Forsyth:
Swapout: Learning an ensemble of deep architectures. 28-36 - Richard Nock:
On Regularizing Rademacher Observation Losses. 37-45 - Ohad Shamir:
Without-Replacement Sampling for Stochastic Gradient Methods. 46-54 - Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause:
Fast and Provably Good Seedings for k-Means. 55-63 - Chelsea Finn, Ian J. Goodfellow, Sergey Levine:
Unsupervised Learning for Physical Interaction through Video Prediction. 64-72 - Ehsan Elhamifar:
High-Rank Matrix Completion and Clustering under Self-Expressive Models. 73-81 - Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, Josh Tenenbaum:
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. 82-90 - Tianfan Xue, Jiajun Wu, Katherine L. Bouman, Bill Freeman:
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks. 91-99 - Pedro A. Ortega, Alan A. Stocker:
Human Decision-Making under Limited Time. 100-108 - Shizhong Han, Zibo Meng, Ahmed-Shehab Khan, Yan Tong:
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition. 109-117 - Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Natural-Parameter Networks: A Class of Probabilistic Neural Networks. 118-126 - Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Feng Lu, Shuicheng Yan:
Tree-Structured Reinforcement Learning for Sequential Object Localization. 127-135 - Mingsheng Long
, Han Zhu, Jianmin Wang, Michael I. Jordan:
Unsupervised Domain Adaptation with Residual Transfer Networks. 136-144 - Zohar S. Karnin:
Verification Based Solution for Structured MAB Problems. 145-153 - Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen:
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games. 154-162 - Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham:
Linear dynamical neural population models through nonlinear embeddings. 163-171 - Peng Wang, Xiaohui Shen, Bryan C. Russell, Scott Cohen, Brian L. Price, Alan L. Yuille:
SURGE: Surface Regularized Geometry Estimation from a Single Image. 172-180 - Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. 181-189 - Edouard Pauwels, Jean B. Lasserre:
Sorting out typicality with the inverse moment matrix SOS polynomial. 190-198 - Zohar S. Karnin, Oren Anava:
Multi-armed Bandits: Competing with Optimal Sequences. 199-207 - Ruth Heller, Yair Heller:
Multivariate tests of association based on univariate tests. 208-216 - Scott E. Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee:
Learning What and Where to Draw. 217-225 - Damek Davis, Brent Edmunds, Madeleine Udell:
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. 226-234 - Hakan Bilen, Andrea Vedaldi:
Integrated perception with recurrent multi-task neural networks. 235-243 - Yu-Xiong Wang, Martial Hebert:
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs. 244-252 - Yunhe Wang, Chang Xu, Shan You, Dacheng Tao, Chao Xu:
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain. 253-261 - Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause:
Cooperative Graphical Models. 262-270 - Sebastian Nowozin, Botond Cseke, Ryota Tomioka:
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. 271-279 - Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. 280-288 - Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh:
Hierarchical Question-Image Co-Attention for Visual Question Answering. 289-297 - Malik Magdon-Ismail, Christos Boutsidis:
Optimal Sparse Linear Encoders and Sparse PCA. 298-306 - Yangyan Li, Sören Pirk, Hao Su, Charles Ruizhongtai Qi, Leonidas J. Guibas:
FPNN: Field Probing Neural Networks for 3D Data. 307-315 - Xiao Chu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang:
CRF-CNN: Modeling Structured Information in Human Pose Estimation. 316-324 - Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. 325-333 - Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. 334-342 - Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan:
Domain Separation Networks. 343-351 - Diane Bouchacourt, Pawan Kumar Mudigonda, Sebastian Nowozin:
DISCO Nets : DISsimilarity COefficients Networks. 352-360 - Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, JungWoo Ha, Byoung-Tak Zhang:
Multimodal Residual Learning for Visual QA. 361-369 - Oswin Krause, Dídac Rodríguez Arbonès, Christian Igel:
CMA-ES with Optimal Covariance Update and Storage Complexity. 370-378 - Jifeng Dai, Yi Li, Kaiming He, Jian Sun:
R-FCN: Object Detection via Region-based Fully Convolutional Networks. 379-387 - Eugène Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
GAP Safe Screening Rules for Sparse-Group Lasso. 388-396 - Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez-Rodriguez:
Learning and Forecasting Opinion Dynamics in Social Networks. 397-405 - Rong Zhu:
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares. 406-414 - Hao Wang, Xingjian Shi, Dit-Yan Yeung:
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks. 415-423 - Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborová:
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula. 424-432 - Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. 433-441 - Junhua Mao, Jiajing Xu, Yushi Jing, Alan L. Yuille:
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. 442-450 - Yiming Ying, Longyin Wen, Siwei Lyu:
Stochastic Online AUC Maximization. 451-459 - Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
The Generalized Reparameterization Gradient. 460-468 - Ming-Yu Liu, Oncel Tuzel:
Coupled Generative Adversarial Networks. 469-477 - Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. 478-486 - Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Variational Information Maximization for Feature Selection. 487-495 - Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. 496-504 - Vu C. Dinh, Lam Si Tung Ho, Binh T. Nguyen, Duy M. H. Nguyen:
Fast learning rates with heavy-tailed losses. 505-513 - Kaito Fujii, Hisashi Kashima:
Budgeted stream-based active learning via adaptive submodular maximization. 514-522 - Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi:
Learning feed-forward one-shot learners. 523-531 - Ting-Yu Cheng, Guiguan Lin, Xinyang Gong, Kang-Jun Liu, Shan-Hung Wu:
Learning User Perceived Clusters with Feature-Level Supervision. 532-540 - Pan Zhang:
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization. 541-549 - Andreas Veit, Michael J. Wilber, Serge J. Belongie:
Residual Networks Behave Like Ensembles of Relatively Shallow Networks. 550-558 - Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart:
Adversarial Multiclass Classification: A Risk Minimization Perspective. 559-567 - Gang Wang, Georgios B. Giannakis:
Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow. 568-576 - Francesco Orabona, Dávid Pál:
Coin Betting and Parameter-Free Online Learning. 577-585 - Kenji Kawaguchi:
Deep Learning without Poor Local Minima. 586-594 - Eugene Belilovsky, Gaël Varoquaux, Matthew B. Blaschko:
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity. 595-603 - Dennis Wei:
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++. 604-612 - Carl Vondrick, Hamed Pirsiavash, Antonio Torralba:
Generating Videos with Scene Dynamics. 613-621 - Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman:
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks. 622-630 - Kun He, Yan Wang, John E. Hopcroft:
A Powerful Generative Model Using Random Weights for the Deep Image Representation. 631-639 - Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán:
Optimizing affinity-based binary hashing using auxiliary coordinates. 640-648 - Huasen Wu, Xin Liu:
Double Thompson Sampling for Dueling Bandits. 649-657 - Alexey Dosovitskiy, Thomas Brox:
Generating Images with Perceptual Similarity Metrics based on Deep Networks. 658-666 - Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc Van Gool:
Dynamic Filter Networks. 667-675 - Aaron Defazio:
A Simple Practical Accelerated Method for Finite Sums. 676-684 - Conghui Tan, Shiqian Ma, Yu-Hong Dai, Yuqiu Qian:
Barzilai-Borwein Step Size for Stochastic Gradient Descent. 685-693 - Guillaume Papa, Aurélien Bellet, Stéphan Clémençon:
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. 694-702 - Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya:
Optimal spectral transportation with application to music transcription. 703-711 - Damien Scieur, Alexandre d'Aspremont, Francis R. Bach:
Regularized Nonlinear Acceleration. 712-720 - Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros:
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. 721-729 - Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng:
Single-Image Depth Perception in the Wild. 730-738 - Ashish Khetan, Sewoong Oh:
Computational and Statistical Tradeoffs in Learning to Rank. 739-747 - Ashok Cutkosky, Kwabena Boahen:
Online Convex Optimization with Unconstrained Domains and Losses. 748-756 - Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei:
An ensemble diversity approach to supervised binary hashing. 757-765 - Weiran Wang, Jialei Wang, Dan Garber, Nati Srebro:
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. 766-774 - Kevin G. Jamieson, Daniel Haas, Benjamin Recht:
The Power of Adaptivity in Identifying Statistical Alternatives. 775-783 - Aurélien Garivier, Tor Lattimore, Emilie Kaufmann:
On Explore-Then-Commit strategies. 784-792 - Zhao Song, David P. Woodruff, Huan Zhang:
Sublinear Time Orthogonal Tensor Decomposition. 793-801 - Xiangyu Wang, David B. Dunson, Chenlei Leng:
DECOrrelated feature space partitioning for distributed sparse regression. 802-810 - Jinzhuo Wang, Wenmin Wang, Xiongtao Chen, Ronggang Wang, Wen Gao:
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition. 811-819 - Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma:
Dual Learning for Machine Translation. 820-828 - Jason Weston:
Dialog-based Language Learning. 829-837 - Théodore Bluche:
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition. 838-846 - Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon:
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction. 847-855 - Aryeh Kontorovich, Sivan Sabato, Ruth Urner:
Active Nearest-Neighbor Learning in Metric Spaces. 856-864 - Shenlong Wang, Sanja Fidler, Raquel Urtasun:
Proximal Deep Structured Models. 865-873 - Dan Garber:
Faster Projection-free Convex Optimization over the Spectrahedron. 874-882 - Rémi Lam, Karen Willcox, David H. Wolpert:
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. 883-891 - Yusuf Aytar, Carl Vondrick, Antonio Torralba:
SoundNet: Learning Sound Representations from Unlabeled Video. 892-900 - Tim Salimans, Diederik P. Kingma:
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. 901 - Haipeng Luo, Alekh Agarwal, Nicolò Cesa-Bianchi, John Langford:
Efficient Second Order Online Learning by Sketching. 902-910 - Yoshinobu Kawahara:
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis. 911-919 - Shandian Zhe, Kai Zhang, Pengyuan Wang, Kuang-chih Lee, Zenglin Xu, Yuan Qi, Zoubin Ghahramani:
Distributed Flexible Nonlinear Tensor Factorization. 920-928 - Pingfan Tang, Jeff M. Phillips:
The Robustness of Estimator Composition. 929-937 - Bipin Rajendran, Pulkit Tandon, Yash H. Malviya:
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats. 938-946 - Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli:
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. 947-955 - Kentaro Minami, Hiromi Arai, Issei Sato, Hiroshi Nakagawa:
Differential Privacy without Sensitivity. 956-964 - Se-Young Yun, Alexandre Proutière:
Optimal Cluster Recovery in the Labeled Stochastic Block Model. 965-973 - Zeyuan Allen Zhu, Yuanzhi Li:
Even Faster SVD Decomposition Yet Without Agonizing Pain. 974-982 - Xinan Wang, Sanjoy Dasgupta:
An algorithm for L1 nearest neighbor search via monotonic embedding. 983-991 - Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. 992-1000 - Dan Garber, Ofer Meshi:
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes. 1001-1009 - Shashank Singh, Simon S. Du, Barnabás Póczos:
Efficient Nonparametric Smoothness Estimation. 1010-1018 - Yarin Gal, Zoubin Ghahramani:
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. 1019-1027 - George Papamakarios, Iain Murray:
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation. 1028-1036 - Arild Nøkland:
Direct Feedback Alignment Provides Learning in Deep Neural Networks. 1037-1045 - Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare:
Safe and Efficient Off-Policy Reinforcement Learning. 1046-1054 - Albert S. Berahas, Jorge Nocedal, Martin Takác:
A Multi-Batch L-BFGS Method for Machine Learning. 1055-1063