Papers
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Hilal Asi, John C. Duchi
Graph Cross Networks with Vertex Infomax Pooling
Maosen Li, Siheng Chen, Ya Zhang et al.
Neural FFTs for Universal Texture Image Synthesis
Morteza Mardani, Guilin Liu, Aysegul Dundar et al.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
Sang-gil Lee, Sungwon Kim, Sungroh Yoon
Using noise to probe recurrent neural network structure and prune synapses
Eli Moore, Rishidev Chaudhuri
Learning Deep Attribution Priors Based On Prior Knowledge
Ethan Weinberger, Joseph Janizek, Su-In Lee
Online Planning with Lookahead Policies
Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Minjia Zhang, Yuxiong He
Smoothly Bounding User Contributions in Differential Privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao et al.
Directional Pruning of Deep Neural Networks
Shih-Kang Chao, Zhanyu Wang, Yue Xing et al.
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer et al.
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Kion Fallah, Adam Willats, Ninghao Liu et al.
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
Zhenyu Liao, Romain Couillet, Michael W. Mahoney
RANet: Region Attention Network for Semantic Segmentation
Dingguo Shen, Yuanfeng Ji, Ping Li et al.
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
Zhuoran Yang, Chi Jin, Zhaoran Wang et al.
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos, Kristof Meding, Felix A. Wichmann
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Marc Finzi, Ke Alexander Wang, Andrew G Wilson
Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park, Jinwoo Shin
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
Akash Saha, Balamurugan Palaniappan
Transferable Graph Optimizers for ML Compilers
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi et al.
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
Mehdi Rezaee, Francis Ferraro
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics
Luca De Gennaro Aquino, Stephan Eckstein