Papers
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Feng Zhu, Andrew Sedler, Harrison A Grier et al.
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
Hengrui Cai, Chengchun Shi, Rui Song et al.
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
Taiji Suzuki, Atsushi Nitanda
Deep Learning on a Data Diet: Finding Important Examples Early in Training
Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
Deep Learning Through the Lens of Example Difficulty
Robert Baldock, Hartmut Maennel, Behnam Neyshabur
Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar et al.
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks
Woochul Kang, Daeyeon Kim
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
Tianchang Shen, Jun Gao, Kangxue Yin et al.
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Amirreza Farnoosh, Sarah Ostadabbas
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
Shuwen Yang, Ziyao Li, Guojie Song et al.
Deep Networks Provably Classify Data on Curves
Tingran Wang, Sam Buchanan, Dar Gilboa et al.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Vincent Dutordoir, James Hensman, Mark van der Wilk et al.
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
Hang Xu, Kelly Kostopoulou, Aritra Dutta et al.
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro et al.
Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen et al.
Deep Self-Dissimilarities as Powerful Visual Fingerprints
Idan Kligvasser, Tamar Shaham, Yuval Bahat et al.
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Brandon Jacques, Zoran Tiganj, Marc Howard et al.
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
Rui Lin, Jie Ran, King Hung Chiu et al.
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Ligeng Zhu, Hongzhou Lin, Yao Lu et al.
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
Wenqing Zheng, Qiangqiang Guo, Hao Yang et al.
Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari et al.
Denoising Normalizing Flow
Christian Horvat, Jean-Pascal Pfister
Dense Keypoints via Multiview Supervision
Zhixuan Yu, Haozheng Yu, Long Sha et al.