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Task-based end-to-end model learning in stochastic optimization
A method for training neural networks that are provably robust to adversarial attacks.
Sequence modeling benchmarks and temporal convolutional networks
PyTorch wrapper for FFTs
A differentiable LCP physics engine in PyTorch.
OptNet: Differentiable Optimization as a Layer in Neural Networks
A fast and differentiable QP solver for PyTorch.
Code for "Gradient descent GAN optimization is locally stable"
Input Convex Neural Networks
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A Newton ADMM based solver for Cone programming.
The Mixing method: coordinate descent for low-rank semidefinite programming
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
dreaml: dynamic reactive machine learning