most citedCold Diffusion: Inverting Arbitrary Image Transforms Without Noise

105 citations · 126 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2023

Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing

Tahseen Rabbani, Marco Bornstein, Furong Huang

Locality-sensitive hashing (LSH) based frameworks have been used efficiently to select weight vectors in a dense hidden layer with high cosine similarity to an input, enabling dyna…

cs.LG2023

Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function

Ruijie Zheng, Xiyao Wang, Huazhe Xu +1

Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance…

cs.LG202310 cited

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

Yuancheng Xu, Yanchao Sun, Micah Goldblum +2

The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust tr…

cs.LG20234 cited

SMART: Self-supervised Multi-task pretrAining with contRol Transformers

Yanchao Sun, Shuang Ma, Ratnesh Madaan +3

Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining repr…

cs.CV2022105 cited

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Arpit Bansal, Eitan Borgnia, Hong-Min Chu +6

Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative beh…

cs.LG20225 cited

Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh +4

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained commu…