55 citations · 283 across the 50 of their papers we have counts for
7 papers · 1 filter
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…
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Miguel Angel Bautista, Pengsheng Guo, Samira Abnar +9
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle thi…
Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram +7
Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of thei…
Efficient Representation Learning via Adaptive Context Pooling
Chen Huang, Walter Talbott, Navdeep Jaitly +1
Self-attention mechanisms model long-range context by using pairwise attention between all input tokens. In doing so, they assume a fixed attention granularity defined by the indiv…
The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
Vimal Thilak, Etai Littwin, Shuangfei Zhai +3
The grokking phenomenon as reported by Power et al. ( arXiv:2201.02177 ) refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfe…
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…