Importance Weighted Autoencoders: Beyond the Standard VAE
The key difference between multi-sample VAEs and IWAEs: how log-of-averages creates a tighter bound on log-likelihood.
Pytorch
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 Potential: A PyTorch ML Testbed
GPU-accelerated PyTorch framework for the Müller-Brown potential with JIT compilation and Langevin dynamics....
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
Production-grade Word2Vec in PyTorch with vectorized Hierarchical Softmax, Negative Sampling, and torch.compile support....
Modern PyTorch Techniques for VAEs: A Hands-On Tutorial
Learn to implement VAEs in PyTorch: ELBO objective, reparameterization trick, loss scaling, and MNIST experiments on …
Kabsch Algorithm: NumPy, PyTorch, TensorFlow, and JAX
Learn about the Kabsch algorithm for optimal point alignment with implementations in NumPy, PyTorch, TensorFlow, and JAX …
IQCRNN: Certified Stability for Neural Networks
PyTorch IQCRNN enforcing stability guarantees on RNNs via Integral Quadratic Constraints and semidefinite programming....