Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
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Updated
Jul 16, 2024 - Python
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Official implementation of ICML'24 paper "Offline Multi-Objective Optimization".
FelooPy: Efficient & Feature-Rich Integrated Decision Environment
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
Library for Jacobian descent with PyTorch. It enables optimization of neural networks with multiple losses (e.g. multi-task learning).
A tiny project to use ax-platform for multi-objective Bayesian Optimization on OpenFOAM cases
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA.
Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.
Official implementation of IJCAI'24 paper "Peptide Vaccine Design by Evolutionary Multi-objective Optimization."
Official implementation of PPSN'24 paper "Biased Pareto Optimization for Subset Selection with Dynamic Cost Constraints"
🛍 A real-world e-commerce dataset for session-based recommender systems research.
🤹 MultiTRON: Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems, submitted to ACM RecSys 2024.
A PyTorch Library for Learning Pareto Front of Multi-Objective Problem
Problem tailored multi-objective optimization in Python
A PyTorch Library for Multi-Task Learning
Python bindings for OptFrame C++ Functional Core
Generalized and Efficient Blackbox Optimization System
Transforming Neural Architecture Search (NAS) into multi-objective optimization problems. A benchmark suite for testing evolutionary algorithms in deep learning.
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