GitHub

Stein Variational Gradient Descent (SVGD)

SVGD is a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. SVGD iteratively transports a set of particles to match with the target ...
IEEE

Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

Abstract: Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.
IEEE

Robust FFT-Based Scale-Invariant Image Registration with Image Gradients

Abstract: We present a robust FFT-based approach to scale-invariant image registration. Our method relies on FFT-based correlation twice: once in the log-polar Fourier domain to estimate the scaling ...
GitHub

Saving memory using gradient-checkpointing

Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with ...
C&EN

Analytic Gradients for Complete Active Space Pair-Density Functional Theory

† Department of Chemistry, Chemical Theory Center, and the Minnesota Supercomputing Institute, The University of Minnesota, Minneapolis, Minnesota 55455, United States ‡ Department of ...