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Carnegie Mellon
Highlights
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Stars
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Face recognition with deep neural networks.
A Python-embedded modeling language for convex optimization problems.
Sequence modeling benchmarks and temporal convolutional networks
Tutorials and implementations for "Self-normalizing networks"
OptNet: Differentiable Optimization as a Layer in Neural Networks
[ICLR'19] Trellis Networks for Sequence Modeling
Bridging deep learning and logical reasoning using a differentiable satisfiability solver.
Provable adversarial robustness at ImageNet scale
A method for training neural networks that are provably robust to adversarial attacks.
An intelligent block matrix library for numpy, PyTorch, and beyond.
A differentiable LCP physics engine in PyTorch.
A Newton ADMM based solver for Cone programming.