18 citations · 27 across the 15 of their papers we have counts for
6 papers · 1 filter
Explanations that reveal all through the definition of encoding
Aahlad Puli, Nhi Nguyen, Rajesh Ranganath
Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power…
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
Adriel Saporta, Aahlad Puli, Mark Goldstein +1
Contrastive learning methods, such as CLIP, leverage naturally paired data-for example, images and their corresponding text captions-to learn general representations that transfer…
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Reversing a diffusion process by learning its score forms the heart of diffusion-based generative modeling and for estimating properties of scientific systems. The diffusion proces…
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
Chen-Yu Yen, Raghav Singhal, Umang Sharma +3
Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor re…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Abhijith Gandrakota, Lily Zhang, Aahlad Puli +4
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two o…