Pytorch

Discover how Importance Weighted Autoencoders (IWAEs) use the same architecture as VAEs with a fundamentally more powerful objective to leverage multiple samples effectively.

Step-by-step implementation of the classic Müller-Brown potential in PyTorch, with performance comparisons between analytical and automatic differentiation approaches for molecular dynamics and machine learning applications.

Observe confined particle motion in the deep reactant well of the Müller-Brown potential. This simulation demonstrates thermal motion within a stable energy minimum at -146.70 kJ/mol.

Watch particle dynamics in the product minimum of the Müller-Brown potential. This simulation shows intermediate thermal motion behavior at -108.17 kJ/mol energy level.

A high-performance, GPU-accelerated PyTorch testbed for ML-MD algorithms featuring JIT-compiled analytical Jacobian force kernels achieving 3-10x speedup over autograd, robust Langevin dynamics with Velocity-Verlet integration, and modular architecture designed as ground-truth validation for novel machine learning approaches in molecular dynamics.

Experience rare transition events between energy basins in this extended Müller-Brown simulation. Watch as particles overcome energy barriers to explore different regions of the potential energy landscape.

A ground-up PyTorch implementation of Word2Vec treating it as a systems engineering challenge, with “tensorized tree” architecture converting pointer-chasing Hierarchical Softmax into dense GPU operations, infinite streaming datasets with Zipfian subsampling, and torch.compile compatibility for production-grade efficiency.

A complete guide to implementing modern Variational Autoencoders in PyTorch. Includes a copy-pasteable implementation, explanation of KL annealing to fix posterior collapse, and a deep dive into stable standard deviation parameterizations.

Learn to align molecular structures and point clouds using the Kabsch algorithm, with differentiable implementations for modern ML frameworks.

A PyTorch implementation enforcing strict Lyapunov stability guarantees on recurrent neural network controllers through Integral Quadratic Constraints, bridging 1990s robust control theory with modern deep reinforcement learning by solving semidefinite programs inside the gradient descent loop to provide mathematical certificates of safety.