Asia
Wasserstein Dependency Measure for Representation Learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet
Neural Information Processing Systems http://nips.cc/
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
Normalization Helps Training of Quantized LSTM
Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, Tie-Yan Liu
However, existing quantization methods usually have inferior performancewhenusedonLSTMs.
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].
Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Pรผschel, Martin Vechev
Neural Information Processing Systems http://nips.cc/
Transformer Doctor: Diagnosing and Treating Vision Transformers
Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision.
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Zehong Hu, Yitao Liang, Jie Zhang, Zhao Li, Yang Liu
Neural Information Processing Systems http://nips.cc/