22 citations · 68 across the 13 of their papers we have counts for
17 papers
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational au…
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
Ruixiang Zhang, Shuangfei Zhai, Etai Littwin +1
In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural…
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation
Martin Bertran, Walter Talbott, Nitish Srivastava +1
Learning generalizeable policies from visual input in the presence of visual distractions is a challenging problem in reinforcement learning. Recently, there has been renewed inter…
Regularized Training of Nearest Neighbor Language Models
Jean-Francois Ton, Walter Talbott, Shuangfei Zhai +1
Including memory banks in a natural language processing architecture increases model capacity by equipping it with additional data at inference time. In this paper, we build upon $…
Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks
Shih-Yu Sun, Vimal Thilak, Etai Littwin +2
Deep linear networks trained with gradient descent yield low rank solutions, as is typically studied in matrix factorization. In this paper, we take a step further and analyze impl…
Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks
Etai Littwin, Omid Saremi, Shuangfei Zhai +4
We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck. Unlike the neural tangent kernel limit, a bottleneck in an otherwise infinite w…