3 papers
cs.LG2026
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
Noah Schwartz, Chandra Kanth Nagesh, Sriram Sankaranarayanan +3
We present a generalized framework for the range verification of neural networks featuring non-linear activation functions. Our approach first constructs an ``optimized piecewise a…
cs.LG2025
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur +2
We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to…
cs.AI2024
Anticipating Oblivious Opponents in Stochastic Games
Shadi Tasdighi Kalat, Sriram Sankaranarayanan, Ashutosh Trivedi
We present an approach for systematically anticipating the actions and policies employed by \emph{oblivious} environments in concurrent stochastic games, while maximizing a reward…