Importance Weighted Autoencoders: Beyond the Standard VAE
Learn the crucial difference between multi-sample VAEs and Importance Weighted Autoencoders (IWAEs). Explore how …
Pytorch
High-Performance Müller-Brown Potential: A PyTorch Testbed for Machine Learning and Molecular Dynamics
High-performance, GPU-accelerated PyTorch framework for the Müller-Brown potential, featuring JIT compilation, …...
Implementing the Müller-Brown Potential in PyTorch
Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …
Müller-Brown Basin MA: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the deep reactant minimum (Basin MA) of the Müller-Brown …
Müller-Brown Basin MB: Langevin Dynamics Simulation
Langevin dynamics simulation showing particle motion in the product minimum (Basin MB) of the Müller-Brown potential …
Müller-Brown Transition: Langevin Dynamics Simulation
Extended Langevin dynamics simulation showing particle transitions between different basins of the Müller-Brown …
Vectorizing the Tree: High-Performance Word2Vec in Pure PyTorch
A production-grade implementation of Hierarchical Softmax and Negative Sampling, featuring vectorized tree traversals, …...
Modern PyTorch Techniques for VAEs: A Hands-On Tutorial
A comprehensive guide to implementing Variational Autoencoders (VAEs) in PyTorch. Covers the ELBO objective, …
Kabsch Algorithm: NumPy, PyTorch, TensorFlow, and JAX
Learn about the Kabsch algorithm for optimal point alignment with implementations in NumPy, PyTorch, TensorFlow, and JAX …
Certified Robustness: Projecting Neural Networks onto Stability Constraints
A PyTorch re-implementation of IQCRNN, enforcing strict stability guarantees on Recurrent Neural Networks via Integral …...