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ORCIDNeurIPS 2019: Vancouver, BC, Canada
- Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 - Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim:
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. 1-12 - Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee:
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. 13-23 - Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. 24-34 - Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian D. Reid:
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video. 35-45 - Hyeonwoo Yu, Beomhee Lee:
Zero-shot Learning via Simultaneous Generating and Learning. 46-56 - Brian Lubars, Chenhao Tan:
Ask not what AI can do, but what AI should do: Towards a framework of task delegability. 57-67 - Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens:
Stand-Alone Self-Attention in Vision Models. 68-80 - Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee:
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. 81-91 - Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, Honglak Lee:
Unsupervised learning of object structure and dynamics from videos. 92-102 - Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen:
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. 103-112 - Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. 113-124 - Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Adversarial Examples Are Not Bugs, They Are Features. 125-136 - Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian D. Reid, Hamid Rezatofighi, Silvio Savarese:
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. 137-146 - Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye:
FreeAnchor: Learning to Match Anchors for Visual Object Detection. 147-155 - Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. 156-167 - Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. 168-180 - Mark Bun, Thomas Steinke:
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. 181-191 - Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Alan Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré:
Multi-Resolution Weak Supervision for Sequential Data. 192-203 - Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox:
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. 204-214 - Vladimir V. Kniaz, Vladimir A. Knyaz, Fabio Remondino:
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection. 215-226 - Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. 227-238 - Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan:
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement. 239-249 - Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. 250-260 - Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde:
Generalized Sliced Wasserstein Distances. 261-272 - Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. 273-283 - Sefi Bell-Kligler, Assaf Shocher, Michal Irani:
Blind Super-Resolution Kernel Estimation using an Internal-GAN. 284-293 - Alexandre Louis Lamy, Ziyuan Zhong:
Noise-tolerant fair classification. 294-305 - Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou:
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection. 306-316 - Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang:
Joint-task Self-supervised Learning for Temporal Correspondence. 317-327 - Justin Domke:
Provable Gradient Variance Guarantees for Black-Box Variational Inference. 328-337 - Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. 338-347 - David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne:
Experience Replay for Continual Learning. 348-358 - Boris Hanin, David Rolnick:
Deep ReLU Networks Have Surprisingly Few Activation Patterns. 359-368 - Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee:
Chasing Ghosts: Instruction Following as Bayesian State Tracking. 369-379 - Yu Sun, Jiaming Liu, Ulugbek Kamilov:
Block Coordinate Regularization by Denoising. 380-390 - Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien:
Reducing Noise in GAN Training with Variance Reduced Extragradient. 391-401 - Zihan Li, Matthias Fresacher, Jonathan Scarlett:
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries. 402-412 - Hisham Husain, Richard Nock, Robert C. Williamson:
A Primal-Dual link between GANs and Autoencoders. 413-422 - Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu:
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking. 423-432 - Qiming Zhang, Jing Zhang, Wei Liu, Dacheng Tao:
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. 433-443 - Patrick Putzky, Max Welling:
Invert to Learn to Invert. 444-454 - Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras:
Equitable Stable Matchings in Quadratic Time. 455-465 - Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez:
Zero-Shot Semantic Segmentation. 466-477 - Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray:
Metric Learning for Adversarial Robustness. 478-489 - Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, Ulrich Neumann:
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction. 490-500 - Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou:
Batched Multi-armed Bandits Problem. 501-511 - Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang:
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. 512-522 - Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Linear Regression. 523-533 - Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu:
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos. 534-544 - Bichuan Guo, Yuxing Han, Jiangtao Wen:
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling. 545-556 - Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu:
CPM-Nets: Cross Partial Multi-View Networks. 557-567 - Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, Hongsheng Li:
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis. 568-578 - Andrey Kolobov, Yuval Peres, Cheng Lu, Eric Horvitz:
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling. 579-589 - Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell:
SySCD: A System-Aware Parallel Coordinate Descent Algorithm. 590-600 - Artem Sobolev, Dmitry P. Vetrov:
Importance Weighted Hierarchical Variational Inference. 601-613 - Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik:
RSN: Randomized Subspace Newton. 614-623 - Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan:
Trust Region-Guided Proximal Policy Optimization. 624-634 - Dina Bashkirova, Ben Usman, Kate Saenko:
Adversarial Self-Defense for Cycle-Consistent GANs. 635-645 - Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. 646-656 - Armin Lederer, Jonas Umlauft, Sandra Hirche:
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control. 657-667 - Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang:
ETNet: Error Transition Network for Arbitrary Style Transfer. 668-677 - Max Vladymyrov:
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. 678-687 - Shaojie Bai, J. Zico Kolter, Vladlen Koltun:
Deep Equilibrium Models. 688-699 - Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. 700-712 - Miaoyan Wang, Yuchen Zeng:
Multiway clustering via tensor block models. 713-723 - Wang Chi Cheung:
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives. 724-734 - Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang:
NAT: Neural Architecture Transformer for Accurate and Compact Architectures. 735-747 - Ruidi Chen, Ioannis Ch. Paschalidis:
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression. 748-758 - Xuanyi Dong, Yi Yang:
Network Pruning via Transformable Architecture Search. 759-770 - Junbang Liang, Ming C. Lin, Vladlen Koltun:
Differentiable Cloth Simulation for Inverse Problems. 771-780 - Aaron Schein, Scott W. Linderman, Mingyuan Zhou
, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. 781-792 - Gengshan Yang, Deva Ramanan:
Volumetric Correspondence Networks for Optical Flow. 793-803 - Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu:
Learning Conditional Deformable Templates with Convolutional Networks. 804-816 - Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu:
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data. 817-827 - Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. 828-838 - Rémi Cadène, Corentin Dancette, Hédi Ben-Younes, Matthieu Cord, Devi Parikh:
RUBi: Reducing Unimodal Biases for Visual Question Answering. 839-850 - Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang:
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition. 851-863 - Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma:
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution. 864-873 - Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan:
DATA: Differentiable ArchiTecture Approximation. 874-884 - Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao:
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge. 885-895 - Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu:
Memory-oriented Decoder for Light Field Salient Object Detection. 896-906 - Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen:
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. 907-917 - Natalia Neverova, David Novotný, Andrea Vedaldi:
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. 918-926 - Chris Wendler, Markus Püschel, Dan Alistarh:
Powerset Convolutional Neural Networks. 927-938 - Arsenii Vanunts, Alexey Drutsa:
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer. 939-951 - Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. 952-962 - Zhijian Liu, Haotian Tang, Yujun Lin, Song Han:
Point-Voxel CNN for Efficient 3D Deep Learning. 963-973 - Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning without Weight Transport. 974-982 - Aadirupa Saha, Aditya Gopalan:
Combinatorial Bandits with Relative Feedback. 983-993 - Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao:
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme. 994-1004 - Leonidas J. Guibas, Qixing Huang, Zhenxiao Liang:
A Condition Number for Joint Optimization of Cycle-Consistent Networks. 1005-1015 - Nicki Skafte Detlefsen, Søren Hauberg:
Explicit Disentanglement of Appearance and Perspective in Generative Models. 1016-1026 - Hédi Hadiji:
Polynomial Cost of Adaptation for X-Armed Bandits. 1027-1036 - Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning to Propagate for Graph Meta-Learning. 1037-1048 - Sepehr Assadi, Eric Balkanski, Renato Paes Leme:
Secretary Ranking with Minimal Inversions. 1049-1061 - Siqi Liu, Milos Hauskrecht:
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. 1062-1072 - Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu:
Learning Perceptual Inference by Contrasting. 1073-1085 - Yu-Chia Chen, Marina Meila:
Selecting the independent coordinates of manifolds with large aspect ratios. 1086-1095 - Zhengyang Shen, François-Xavier Vialard, Marc Niethammer:
Region-specific Diffeomorphic Metric Mapping. 1096-1106 - Chengguang Xu, Ehsan Elhamifar:
Deep Supervised Summarization: Algorithm and Application to Learning Instructions. 1107-1118 - Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein:
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. 1119-1130 - Brett Daley, Christopher Amato:
Reconciling λ-Returns with Experience Replay. 1131-1140 - Fengxiang He, Tongliang Liu, Dacheng Tao:
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. 1141-1150 - Max Simchowitz, Kevin Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. 1151-1160 - Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama:
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. 1161-1170 - Paul Hongsuck Seo, Geeho Kim, Bohyung Han:
Combinatorial Inference against Label Noise. 1171-1181 - Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong:
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. 1182-1191 - Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi:
Convolution with even-sized kernels and symmetric padding. 1192-1203 - Dong Liu, Haochen Zhang, Zhiwei Xiong:
On The Classification-Distortion-Perception Tradeoff. 1204-1213 - Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. 1214-1225 - Holden Lee, Oren Mangoubi, Nisheeth K. Vishnoi:
Online sampling from log-concave distributions. 1226-1237 - Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. 1238-1248 - Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum:
Finding Friend and Foe in Multi-Agent Games. 1249-1259 - Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Image Synthesis with a Single (Robust) Classifier. 1260-1271 - Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu:
Model Compression with Adversarial Robustness: A Unified Optimization Framework. 1283-1294 - Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh:
Cross-channel Communication Networks. 1295-1304 - Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam:
CondConv: Conditionally Parameterized Convolutions for Efficient Inference. 1305-1316 - Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei:
Regression Planning Networks. 1317-1327 - Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxilary Classifiers GAN. 1328-1337 - Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li:
Conditional Structure Generation through Graph Variational Generative Adversarial Nets. 1338-1349 - Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. 1350-1360 - Edith Cohen, Ofir Geri:
Sampling Sketches for Concave Sublinear Functions of Frequencies. 1361-1371 - Pei Wang, Nuno Vasconcelos:
Deliberative Explanations: visualizing network insecurities. 1372-1383 - Eugène Ndiaye, Ichiro Takeuchi:
Computing Full Conformal Prediction Set with Approximate Homotopy. 1384-1393 - Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. 1394-1406 - Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang:
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards. 1407-1417 - Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar:
Multi-View Reinforcement Learning. 1418-1429 - Thang Vu, Hyunjun Jang, Trung X. Pham, Chang Dong Yoo:
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution. 1430-1440 - Jian Sun, Zongben Xu:
Neural Diffusion Distance for Image Segmentation. 1441-1451 - Mete Ozay:
Fine-grained Optimization of Deep Neural Networks. 1452-1462 - Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun:
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. 1463-1473 - Chris Russell, Matteo Toso, Neill D. F. Campbell:
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions. 1474-1484 - Pascal Mettes, Elise van der Pol, Cees Snoek:
Hyperspherical Prototype Networks. 1485-1495 - Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac:
Expressive power of tensor-network factorizations for probabilistic modeling. 1496-1508 - Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar:
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. 1509-1520 - Zhize Li:
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points. 1521-1531 - Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng:
Efficient Meta Learning via Minibatch Proximal Update. 1532-1542 - Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. 1543-1553 - Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti:
Guided Similarity Separation for Image Retrieval. 1554-1564 - Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. 1565-1576 - Yuan Deng, Jon Schneider, Balasubramanian Sivan:
Strategizing against No-regret Learners. 1577-1585 - Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen:
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs. 1586-1598 - Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon:
Hierarchical Optimal Transport for Document Representation. 1599-1609 - Rui Li:
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes. 1610-1619 - Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge J. Belongie:
Positional Normalization. 1620-1632 - Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger:
A New Defense Against Adversarial Images: Turning a Weakness into a Strength. 1633-1644 - Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang:
Quadratic Video Interpolation. 1645-1654 - Bao Wang, Zuoqiang Shi, Stanley J. Osher:
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. 1655-1665 - Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, Yingli Tian, Jan Ernst, Andreas Hutter:
Incremental Scene Synthesis. 1666-1676 - Shikun Liu, Andrew J. Davison, Edward Johns:
Self-Supervised Generalisation with Meta Auxiliary Learning. 1677-1687 - Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang:
Variational Denoising Network: Toward Blind Noise Modeling and Removal. 1688-1699 - Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima:
Fast Sparse Group Lasso. 1700-1708 - Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng:
Learnable Tree Filter for Structure-preserving Feature Transform. 1709-1719 - Yuki Yoshida, Masato Okada:
Data-Dependence of Plateau Phenomenon in Learning with Neural Network - Statistical Mechanical Analysis. 1720-1728 - Talfan Evans, Neil Burgess:
Coordinated hippocampal-entorhinal replay as structural inference. 1729-1741 - Hao Zheng, Faming Fang, Guixu Zhang:
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction. 1742-1752 - Aaron Defazio, Léon Bottou:
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. 1753-1763 - Aaron Defazio:
On the Curved Geometry of Accelerated Optimization. 1764-1773 - Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan:
Multi-marginal Wasserstein GAN. 1774-1784