105 citations · 175 across the 15 of their papers we have counts for
15 papers
ChannelTok: Efficient Flexible-Length Vision Tokenization
Sukriti Paul, Arpit Bansal, Tom Goldstein
Leading flexible vision tokenizers achieve SOTA quality at an extreme cost, relying on parameter-heavy backbones and slow, multi-step generative decoders. We depart from this compl…
Just How Flexible are Neural Networks in Practice?
Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal +3
It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters, underpinning notions of overparameterized and underpara…
Transformers Can Do Arithmetic with the Right Embeddings
Sean McLeish, Arpit Bansal, Alex Stein +8
The poor performance of transformers on arithmetic tasks seems to stem in large part from their inability to keep track of the exact position of each digit inside of a large span o…
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
Hossein Souri, Arpit Bansal, Hamid Kazemi +7
Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can po…
Universal Guidance for Diffusion Models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild +4
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work…
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…
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…
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…
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…
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…
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…
Datasets for Studying Generalization from Easy to Hard Examples
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta +5
We describe new datasets for studying generalization from easy to hard examples.
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova +3
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usuall…
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Liam Fowl, Ping-yeh Chiang, Micah Goldblum +4
Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of releas…
PAG-Net: Progressive Attention Guided Depth Super-resolution Network
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. It is based on residual dense networks and involves the at…