537 Open source projects that are alternatives of or similar to Awesome Automl And Lightweight Models

Personalizing Dialogue Agents via Meta-Learning

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

knowledge distillation papers

pytorch implementation of Structured Bayesian Pruning

Code for meta-learning initializations for image segmentation

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

No description or website provided.

Parallel random matrix tools and complexity for deep learning

Build a full-featured home server or NAS replacement with an Ubuntu box and this playbook.

Automatic machine learning for tabular data. ⚡🔥⚡

Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

A general-purpose pythonic genetic algorithm.

KGy SOFT Drawing is a library for advanced image, icon and graphics handling.

🤖 telegram bot for movies

A curated list of awesome Distributed Deep Learning resources.

🦅Hyperparameter optimization for machine learning pipelines 🦅

[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

Codebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.

Great resources for making Reinforcement Learning work in Real Life situations. Papers,projects and more.

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

Tools for generating mini-ImageNet dataset and processing batches

Efficient Learning of Augmentation Policy Schedules

[ISBI'21] MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

🎨 Simple color palette quantization using MMCQ

🌐👨‍💻💻 Setup your own NAS on a Raspberry Pi

Code for the paper "Evolved Policy Gradients"

Autonomous Performance Tuning for Kubernetes !

Python module to interface with OpenML

Machine learning for Ruby

⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

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

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020

OpenMMLab Model Compression Toolbox and Benchmark.

pytorch implementation of Optimization as a Model for Few-shot Learning

Neural Architecture Search Powered by Swarm Intelligence 🐜

a mini-package-manager for QNAP NAS

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

A treasure chest for image classification powered by PaddlePaddle

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.

State-of-the-art method for large-scale ANN search as of Oct 2016. Presented at ECCV 16.

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

PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.

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

QKeras: a quantization deep learning library for Tensorflow Keras

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

Tornado is a human-in-the-loop machine learning framework that helps you exploit your unlabelled data to train models through a simple and easy to use web interface.

Memory efficient MAML using gradient checkpointing

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

This is an simple example of tagging bank transactions with ML.NET

Set model hyperparameters using deterministic, exact algorithms.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Go library for color quantization and dithering

Black-box optimization library

Official implementation of Meta-StyleSpeech and StyleSpeech

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.