202 Open source projects that are alternatives of or similar to Boml

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"

Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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A fully decentralized hyperparameter optimization framework

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PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

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Purely functional genetic algorithms for multi-objective optimisation

A library for performing hyperparameter optimization

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Tensorflow Implementation of MAML

Hyperparameter optimization for PyTorch.

Code for: "Learning To Adapt For Stereo" accepted at CVPR2019

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Python library for Bayesian hyper-parameters optimization

Simple, but essential Bayesian optimization package

Hyperparameter optimization in Julia.

My notes and assignment solutions for Stanford CS330 (Fall 2019 & 2020) Deep Multi-Task and Meta Learning

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Black box hyperparameter optimization made easy.

Tuning hyperparams fast with Hyperband

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Meta learning with BERT as a learner

Python library for CMA Evolution Strategy.

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

Meta-DETR: Official PyTorch Implementation

A dataset of datasets for learning to learn from few examples

Autonomous Performance Tuning for Kubernetes !

Graph meta learning via local subgraphs (NeurIPS 2020)

This is an official implementation for "Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising"(KDD2021).

A hyperparameter optimization framework

A shell-friendly hyperparameter search tool inspired by Optuna

A modular toolbox for meta-learning research with a focus on speed and reproducibility.

Machine Learning Tool Box

Experimental Global Optimization Algorithm

Tensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.

Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

Your local Flux surgeon

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

A simple collection of well working NLP models (Keras, H2O, StarSpace) tuned and benchmarked on a variety of datasets.

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Awesome Multitask Learning Resources

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

A toolset for black-box hyperparameter optimisation.

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers

Python library for CMA Evolution Strategy.

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀