Asia
A V-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis Susan Liang 1 Chao Huang
This consistency ensures perceptual realism and immersion, enriching the overall user experience.
Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation
In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder.
Preference Based Adaptation for Learning Objectives
Neural Information Processing Systems http://nips.cc/
Optimized Covariance Design for AB Test on Social Network under Interference Qianyi Chen
Online A/B tests have become increasingly popular and important for social platforms.
Is Q-Learning Provably Efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Neural Information Processing Systems http://nips.cc/
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posteriordistribution.