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ORCIDNIPS 2017: Long Beach, CA, USA
- Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017
- Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber:
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. 1-11 - Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath:
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data. 12-23 - Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian D. Reid:
Deep Subspace Clustering Networks. 24-33 - Rohit Girdhar, Deva Ramanan:
Attentional Pooling for Action Recognition. 34-45 - Heinrich Jiang:
On the Consistency of Quick Shift. 46-55 - Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. 56-65 - Jian Zhao, Lin Xiong, Jayashree Karlekar, Jianshu Li, Fang Zhao, Zhecan Wang, Sugiri Pranata, Shengmei Shen, Shuicheng Yan, Jiashi Feng:
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis. 66-76 - Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael J. Witbrock, Mark A. Hasegawa-Johnson, Thomas S. Huang:
Dilated Recurrent Neural Networks. 77-87 - Saurabh Verma, Zhi-Li Zhang:
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs. 88-98 - Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett:
Scalable Generalized Linear Bandits: Online Computation and Hashing. 99-109 - Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami:
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. 110-118 - Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui
, Julien Stainer:
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. 119-129 - El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer:
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning. 130-140 - Lin Chen, Andreas Krause, Amin Karbasi:
Interactive Submodular Bandit. 141-152 - Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum:
Learning to See Physics via Visual De-animation. 153-164 - Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei:
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks. 165-177 - Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu:
Decoding with Value Networks for Neural Machine Translation. 178-187 - Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao:
Parametric Simplex Method for Sparse Learning. 188-197 - Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang:
Group Sparse Additive Machine. 198-208 - Mark Rowland, Adrian Weller:
Uprooting and Rerooting Higher-Order Graphical Models. 209-218 - Krzysztof Marcin Choromanski, Mark Rowland, Adrian Weller:
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. 219-228 - Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, Adrian Weller:
From Parity to Preference-based Notions of Fairness in Classification. 229-239 - Paroma Varma, Bryan D. He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel L. Rubin, Christopher Ré:
Inferring Generative Model Structure with Static Analysis. 240-250 - Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. 251-261 - Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. 262-271 - Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, Tim P. Vogels:
Cortical microcircuits as gated-recurrent neural networks. 272-283 - Cong Han Lim, Stephen J. Wright:
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms. 284-292 - Nisheeth Srivastava, Edward Vul:
A simple model of recognition and recall memory. 293-301 - Anton Osokin, Francis R. Bach, Simon Lacoste-Julien:
On Structured Prediction Theory with Calibrated Convex Surrogate Losses. 302-313 - Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra:
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model. 314-324 - Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing:
MaskRNN: Instance Level Video Object Segmentation. 325-334 - Jianfeng Wang, Xiaolin Hu:
Gated Recurrent Convolution Neural Network for OCR. 335-344 - Xiaofan Lin, Cong Zhao, Wei Pan:
Towards Accurate Binary Convolutional Neural Network. 345-353 - Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang:
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks. 354-364 - Abhishek Kar, Christian Häne, Jitendra Malik:
Learning a Multi-View Stereo Machine. 365-376 - Jonathan Scarlett, Volkan Cevher:
Phase Transitions in the Pooled Data Problem. 377-385 - Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang:
Universal Style Transfer via Feature Transforms. 386-396 - Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura:
On the Model Shrinkage Effect of Gamma Process Edge Partition Models. 397-405 - Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool:
Pose Guided Person Image Generation. 406-416 - Murat A. Erdogdu, Yash Deshpande, Andrea Montanari:
Inference in Graphical Models via Semidefinite Programming Hierarchies. 417-425 - S. Jalil Kazemitabar, Arash A. Amini, Adam Bloniarz, Ameet Talwalkar:
Variable Importance Using Decision Trees. 426-435 - Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura:
Preventing Gradient Explosions in Gated Recurrent Units. 435-444 - Simon S. Du, Yining Wang, Aarti Singh:
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. 445-455 - Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. 456-464 - Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman:
Toward Multimodal Image-to-Image Translation. 465-476 - Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen M. Chu:
Mixture-Rank Matrix Approximation for Collaborative Filtering. 477-485 - An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. 486-496 - Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu:
Learning with Average Top-k Loss. 497-505 - Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi:
Learning multiple visual domains with residual adapters. 506-516 - Ryan J. Tibshirani:
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions. 517-528 - Yu-Chuan Su, Kristen Grauman:
Learning Spherical Convolution for Fast Features from 360° Imagery. 529-539 - Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum:
MarrNet: 3D Shape Reconstruction via 2.5D Sketches. 540-550 - Ilija Ilievski, Jiashi Feng:
Multimodal Learning and Reasoning for Visual Question Answering. 551-562 - Rizal Fathony, Mohammad Ali Bashiri, Brian D. Ziebart:
Adversarial Surrogate Losses for Ordinal Regression. 563-573 - Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabás Póczos:
Hypothesis Transfer Learning via Transformation Functions. 574-584 - Qizhe Xie, Zihang Dai, Yulun Du, Eduard H. Hovy, Graham Neubig:
Controllable Invariance through Adversarial Feature Learning. 585-596 - Yuanzhi Li, Yang Yuan:
Convergence Analysis of Two-layer Neural Networks with ReLU Activation. 597-607 - Tomoya Murata, Taiji Suzuki:
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization. 608-617 - Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk:
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. 618-626 - Dan Garber:
Efficient Online Linear Optimization with Approximation Algorithms. 627-635 - Shixiang Chen, Shiqian Ma, Wei Liu:
Geometric Descent Method for Convex Composite Minimization. 636-644 - Chris Junchi Li, Mengdi Wang, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. 645-655 - Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. 656-666 - Ilja Kuzborskij, Nicolò Cesa-Bianchi:
Nonparametric Online Regression while Learning the Metric. 667-676 - Novi Quadrianto, Viktoriia Sharmanska:
Recycling Privileged Learning and Distribution Matching for Fairness. 677-688 - Noam Brown, Tuomas Sandholm:
Safe and Nested Subgame Solving for Imperfect-Information Games. 689-699 - Ming-Yu Liu, Thomas M. Breuel, Jan Kautz:
Unsupervised Image-to-Image Translation Networks. 700-708 - Yaoqing Yang, Pulkit Grover, Soummya Kar:
Coded Distributed Computing for Inverse Problems. 709-719 - Zhaobin Kuang, Sinong Geng, David Page:
A Screening Rule for l1-Regularized Ising Model Estimation. 720-731 - Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou:
Improved Dynamic Regret for Non-degenerate Functions. 732-741 - Guobin Chen, Wongun Choi, Xiang Yu, Tony X. Han, Manmohan Chandraker:
Learning Efficient Object Detection Models with Knowledge Distillation. 742-751 - Sagie Benaim, Lior Wolf:
One-Sided Unsupervised Domain Mapping. 752-762 - Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin:
Deep Mean-Shift Priors for Image Restoration. 763-772 - Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. 773-784 - Guangcan Liu, Qingshan Liu, Xiaotong Yuan:
A New Theory for Matrix Completion. 785-794 - Jeremiah Z. Liu, Brent A. Coull:
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. 795-803 - Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys:
Lower bounds on the robustness to adversarial perturbations. 804-813 - Eric Balkanski, Yaron Singer:
Minimizing a Submodular Function from Samples. 814-822 - Long Jin, Justin Lazarow, Zhuowen Tu:
Introspective Classification with Convolutional Nets. 823-833 - Wei Shen, Kai Zhao, Yilu Guo, Alan L. Yuille:
Label Distribution Learning Forests. 834-843 - James Thewlis, Hakan Bilen, Andrea Vedaldi:
Unsupervised learning of object frames by dense equivariant image labelling. 844-855 - Jose M. Alvarez, Mathieu Salzmann:
Compression-aware Training of Deep Networks. 856-867 - Daniel Milstein, Jason Pacheco, Leigh J. Hochberg, John D. Simeral, Beata Jarosiewicz, Erik B. Sudderth:
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. 868-878 - Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S. Yu:
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. 879-888 - Jaime S. Ide, Fabio Augusto Cappabianco, Fábio Augusto Faria, Chiang-shan R. Li:
Detrended Partial Cross Correlation for Brain Connectivity Analysis. 889-897 - Bo Dai, Dahua Lin:
Contrastive Learning for Image Captioning. 898-907 - Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause:
Safe Model-based Reinforcement Learning with Stability Guarantees. 908-918 - Young Hun Jung, Jack Goetz, Ambuj Tewari:
Online multiclass boosting. 919-928 - Sheng Li, Yun Fu:
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation. 929-939 - Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant:
Learning Overcomplete HMMs. 940-949 - Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris:
GP CaKe: Effective brain connectivity with causal kernels. 950-959 - Eran Malach, Shai Shalev-Shwartz:
Decoupling "when to update" from "how to update". 960-970 - Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter:
Self-Normalizing Neural Networks. 971-980 - Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. 981-990 - Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller:
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. 991-1001 - Haw-Shiuan Chang, Erik G. Learned-Miller, Andrew McCallum:
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples. 1002-1012 - Josip Djolonga, Andreas Krause:
Differentiable Learning of Submodular Functions. 1013-1023 - William L. Hamilton, Zhitao Ying, Jure Leskovec:
Inductive Representation Learning on Large Graphs. 1024-1034 - Ehsan Elhamifar, M. Clara De Paolis Kaluza:
Subset Selection and Summarization in Sequential Data. 1035-1045 - Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Question Asking as Program Generation. 1046-1055 - Songbai Yan, Chicheng Zhang:
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces. 1056-1066 - Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Aarti Singh, Barnabás Póczos:
Gradient Descent Can Take Exponential Time to Escape Saddle Points. 1067-1077 - Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya:
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. 1078-1086 - Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. 1087-1098 - Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort:
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding. 1099-1108 - Damien Scieur, Vincent Roulet, Francis R. Bach, Alexandre d'Aspremont:
Integration Methods and Optimization Algorithms. 1109-1118 - Vincent Roulet, Alexandre d'Aspremont:
Sharpness, Restart and Acceleration. 1119-1129 - Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. 1130-1140 - Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool:
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. 1141-1151 - Stéphanie Allassonnière, Juliette Chevallier, Stephane Oudard:
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data. 1152-1160 - Qinshi Wang, Wei Chen:
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications. 1161-1171 - Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell:
Predictive-State Decoders: Encoding the Future into Recurrent Networks. 1172-1183 - Shipra Agrawal, Randy Jia:
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. 1184-1194 - Antti Tarvainen, Harri Valpola:
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. 1195-1204 - Nikolay Savinov, Lubor Ladicky, Marc Pollefeys:
Matching neural paths: transfer from recognition to correspondence search. 1205-1214 - Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön:
Linearly constrained Gaussian processes. 1215-1224 - Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher:
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data. 1225-1234 - Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav S. Sukhatme, Joseph J. Lim:
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. 1235-1245 - Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani:
Learning to Inpaint for Image Compression. 1246-1255 - Anirban Roychowdhury, Srinivasan Parthasarathy:
Adaptive Bayesian Sampling with Monte Carlo EM. 1256-1266 - Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang:
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization. 1267-1277