1 code implementation • 14 Feb 2023 • Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining.
1 code implementation • 19 Oct 2022 • Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein
As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data back to their rightful owners.
3 code implementations • 19 Aug 2022 • Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
1 code implementation • 16 Jul 2022 • Arpit Bansal, Ping-Yeh Chiang, Michael Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, Tom Goldstein
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
1 code implementation • 30 Jun 2022 • Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models.
1 code implementation • CVPR 2022 • Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, Tom Goldstein
We also use decision boundary methods to visualize double descent phenomena.
1 code implementation • 11 Feb 2022 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
no code implementations • 29 Sep 2021 • Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
1 code implementation • 13 Aug 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
We describe new datasets for studying generalization from easy to hard examples.
no code implementations • 17 Jun 2021 • Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications.
no code implementations • 16 Feb 2021 • Liam Fowl, Ping-Yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja, Tom Goldstein
Large organizations such as social media companies continually release data, for example user images.
no code implementations • 22 Nov 2019 • Arpit Bansal, Sankaraganesh Jonna, Rajiv R. Sahay
In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet.