2 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…