30 citations · 81 across the 16 of their papers we have counts for
4 papers · 1 filter
Training Energy-Based Models with Diffusion Contrastive Divergences
Weijian Luo, Hao Jiang, Tianyang Hu +3
Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with…
Forward and Inverse Approximation Theory for Linear Temporal Convolutional Networks
Haotian Jiang, Qianxiao Li
We present a theoretical analysis of the approximation properties of convolutional architectures when applied to the modeling of temporal sequences. Specifically, we prove an appro…
Approximation Rate of the Transformer Architecture for Sequence Modeling
Haotian Jiang, Qianxiao Li
The Transformer architecture is widely applied in sequence modeling applications, yet the theoretical understanding of its working principles remains limited. In this work, we inve…
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Haonan Jia, Xiao Zhang, Jun Xu +4
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency. Stochastic variance-…