127 citations · 175 across the 11 of their papers we have counts for
6 papers · 1 filter
Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints
Avik Pal, Alan Edelman, Christopher Rackauckas
Despite the promise of scientific machine learning (SciML) in combining data-driven techniques with mechanistic modeling, existing approaches for incorporating hard constraints in…
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
Avik Pal, Alan Edelman, Chris Rackauckas
Implicit layer deep learning techniques, like Neural Differential Equations, have become an important modeling framework due to their ability to adapt to new problems automatically…
Continuous Deep Equilibrium Models: Training Neural ODEs faster by integrating them to Infinity
Avik Pal, Alan Edelman, Christopher Rackauckas
Implicit models separate the definition of a layer from the description of its solution process. While implicit layers allow features such as depth to adapt to new scenarios and in…
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
Avik Pal, Yingbo Ma, Viral Shah +1
Democratization of machine learning requires architectures that automatically adapt to new problems. Neural Differential Equations (NDEs) have emerged as a popular modeling framewo…
Emergent Road Rules In Multi-Agent Driving Environments
Avik Pal, Jonah Philion, Yuan-Hong Liao +1
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules"…
TorchGAN: A Flexible Framework for GAN Training and Evaluation
Avik Pal, Aniket Das
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design al…