Diffusion Models With Learned Adaptive Noise

1 code implementation • 20 Dec 2023 • Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov

Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images.

Bayesian Inference Density Estimation

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows

no code implementations • 14 Jun 2022 • Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov

Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians.

Two-sample testing

Backpropagation through Combinatorial Algorithms: Identity with Projection Works

2 code implementations • 30 May 2022 • Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius

Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities.

Density Estimation Graph Matching +3

Scaling Symbolic Methods using Gradients for Neural Model Explanation

2 code implementations • ICLR 2021 • Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation.