Stars
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
[VLDB 2022] Dash application for "Navigating the Labyrinth of Time Series Anomaly Detection"
[VLDB 2023] Model Selection for Anomaly Detection in Time Series
Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experiment…
Self-explanatory tutorials for different model-agnostic and model-specific XAI methods
An open access book on scientific visualization using python and matplotlib
Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems
Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems
Surrogate benchmarks for HPO problems
Notebooks on how to use Distributed Evolutionary Algorithm in Python (DEAP)
Jupyter/IPython notebooks about evolutionary computation.
PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* variants (including evolutionary algorithms, swarm-based random optimizers, pattern search,…
Repository for SEN12MS related codes and utilities
danfenghong / GRN-SNDL
Forked from jiankang1991/GRN-SNDLJian Kang, Ruben Fernandez-Beltran, Danfeng Hong, Jocelyn Chanussot, Antonio Plaza. Graph Relation Network: Modeling Relations between Scenes for Multi-Label Remote Sensing Image Classification and…
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Code for our ICML'2020 paper "Stabilizing Differentiable Architecture Search via Perturbation-based Regularization"
Acceptance rates for the major AI conferences
OpenMMLab Self-Supervised Learning Toolbox and Benchmark
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
mpd37 / GaussianProcessses
Forked from CreedIV/GaussianProcesssesintro and exploration of gaussian processes