activity
20192026
most citedCold Diffusion: Inverting Arbitrary Image Transforms Without Noise

105 citations · 175 across the 15 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.LG2022★ 6 cited

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries

Yuxin Wen, Arpit Bansal, Hamid Kazemi +4

As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data…

cs.CV2022★ 105 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.LG2022★ 11 cited

Certified Neural Network Watermarks with Randomized Smoothing

Arpit Bansal, Ping-yeh Chiang, Michael Curry +5

Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio. Recently, watermarking methods have been extended to deep learning models…

cs.LG2022★ 18 cited

Transfer Learning with Deep Tabular Models

Roman Levin, Valeriia Cherepanova, Avi Schwarzschild +5

Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural…

cs.LG2022★ 3 cited

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective

Gowthami Somepalli, Liam Fowl, Arpit Bansal +5

We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility and general…

cs.LG2022★ 3 cited

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Arpit Bansal, Avi Schwarzschild, Eitan Borgnia +4

Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capab…