konstmish / opt_methods
Benchmarking optimization methods on convex problems.
☆27Updated 6 months ago
Related projects: ⓘ
- PyTorch implementation of Hessian Free optimisation☆43Updated 4 years ago
- simple JAX-/NumPy-based implementations of NGD with exact/approximate Fisher Information Matrix both in parameter-space and function-spac…☆14Updated 3 years ago
- Adaptive gradient descent without descent☆44Updated 2 years ago
- Efficient Riemannian Optimization on Stiefel Manifold via Cayley Transform☆34Updated 5 years ago
- Benchmark for bi-level optimization solvers☆34Updated last month
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated last year
- Attempt to speed Randomized SVD(Singular Value Decomposition) Using pytorch and it's gnu capabilities.☆17Updated 6 years ago
- Computing gradients and Hessians of feed-forward networks with GPU acceleration☆18Updated 7 months ago
- Models and code for the ICLR 2020 workshop paper "Towards Understanding Normalization in Neural ODEs"☆16Updated 4 years ago
- Monotone operator equilibrium networks☆51Updated 4 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 3 years ago
- Implementation of SVRG and SAGA optimization algorithms for deep learning topics.☆68Updated 3 years ago
- ☆11Updated 3 years ago
- "Stochasticity in Neural ODEs: An Empirical Study". Experiments from the paper☆14Updated 4 years ago
- Collect optimizer related papers, data, repositories☆76Updated 9 months ago
- Sampled Quasi-Newton Methods for Deep Learning☆21Updated 4 years ago
- Refining continuous-in-depth neural networks☆39Updated 2 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆18Updated 5 years ago
- Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)☆29Updated 2 years ago
- ☆15Updated 2 years ago
- ☆11Updated 3 years ago
- Stochastic Mirror Descent on CIFAR-10☆17Updated 5 years ago
- orbital MCMC☆10Updated 3 years ago
- Bayesian Optimization with Density-Ratio Estimation☆23Updated last year
- Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)☆22Updated 4 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆86Updated 5 months ago
- PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.☆76Updated last month
- Neural Tangent Kernel Papers☆84Updated 6 months ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆21Updated 11 months ago
- ☆35Updated this week