rohanmohapatra / torchswarm
A fast implementation of Particle Swarm Optimization using PyTorch in GPU
☆29Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for torchswarm
- Companion code for the paper "Learnable Uncertainty under Laplace Approximations" (UAI 2021).☆19Updated 3 years ago
- Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Pap…☆14Updated last year
- Train PyTorch Models using the Genetic Algorithm with PyGAD☆95Updated last month
- This repository contains implementations of some basic sampling methods in numpy.☆65Updated 4 years ago
- Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"☆21Updated 3 years ago
- Code for "Robust Multi-Objective Bayesian Optimization Under Input Noise"☆51Updated 2 years ago
- Dropout as Regularization and Bayesian Approximation☆56Updated 5 years ago
- Training of Neural Network using Particle Swarm Optimization☆29Updated 5 years ago
- Exploring Bayesian Optimization☆72Updated 3 years ago
- Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.☆40Updated 3 years ago
- Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization☆68Updated 2 years ago
- ☆81Updated last year
- [NeurIPS 2019] LOIS: Learning to Optimize In Swarms, guided by posterior estimation☆17Updated 3 years ago
- 💡 Learnergy is a Python library for energy-based machine learning models.☆65Updated 2 weeks ago
- A collection of Hyper parameter tuning library.☆39Updated 4 years ago
- Implementing a Gaussian Process regression model from scratch☆22Updated 3 years ago
- SMOGN: a Pre-processing Approach for Imbalanced Regression - LIDTA2017☆25Updated 7 years ago
- Uncertainty Estimation Using Deep Neural Network and Gradient Boosting Methods☆23Updated 3 years ago
- Official code for UnICORNN (ICML 2021)☆27Updated 3 years ago
- Energy Based Models are a quite novel technique for density estimation. In this university project I explore this new research topic and …☆15Updated 3 years ago
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆115Updated last year
- Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)☆22Updated 2 years ago
- Code for Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization.☆21Updated 3 years ago
- Bayesian optimization with conformal coverage guarantees☆26Updated 2 years ago
- Simple Genetic Programming for Symbolic Regression in Python3☆24Updated 2 years ago
- PointNet-based 3D deep learning model designed for decoding the structure-property relationship for generic crystalline materials.☆10Updated 2 years ago
- ☆10Updated 2 months ago
- Bayesian algorithm execution (BAX)☆46Updated 3 years ago
- Pytorch Optimizer for Simulated Annealing☆23Updated 6 years ago
- Equation Learner, a neural network approach to symbolic regression☆71Updated last month