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

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Automated Machine Learning with scikit-learn

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

Determined: Deep Learning Training Platform

Meta-Feature Extractor

Hierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)

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

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Sequential Model-based Algorithm Configuration

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

Repository for few-shot learning machine learning projects

Memory efficient MAML using gradient checkpointing

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

A general, modular, and programmable architecture search framework

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

Collection for Few-shot Learning

[ICLR'19] Meta-learning with differentiable closed-form solvers

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

a distributed Hyperband implementation on Steroids

"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.

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

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

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

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

A PyTorch Library for Meta-learning Research

A resource list for causality in statistics, data science and physics

A fully decentralized hyperparameter optimization framework

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

Purely functional genetic algorithms for multi-objective optimisation

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

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

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

Simple, but essential Bayesian optimization package

MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

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

Tuning hyperparams fast with Hyperband

Meta learning with BERT as a learner

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

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

A dataset of datasets for learning to learn from few examples

Graph meta learning via local subgraphs (NeurIPS 2020)

A hyperparameter optimization framework

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

Experimental Global Optimization Algorithm

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

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

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

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.

Tensorflow Implementation of MAML

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