What it does This is an awesome-list repo that catalogs papers, code, and projects across five AutoML and efficiency topics: neural architecture search, lightweight model structures, compression and quantization, hyperparameter optimization, and automated feature engineering. Each entry links to the paper and, where available, the implementation.

The interesting bit The value is in the grouping. NAS alone is split by technique—gradient-based, RL, evolutionary, SMBO, random search, hypernetworks, Bayesian optimization—so you can find DARTS or ProxylessNAS without drowning in the literature firehose. The list also explicitly ties papers to their PyTorch or TensorFlow repos, which is rarer than it should be.

Key highlights

• Covers 2017–2019 papers heavily, with some 2018–2019 CVPR/ICLR/NIPS highlights
• NAS section is the deepest, with subcategories by search strategy
• Links directly to implementations (e.g., quark0/darts, MIT-HAN-LAB/ProxylessNAS)
• Includes practical projects like Microsoft NNI and MindsDB
• Explicitly welcomes PRs for missing works

Caveats

• README appears to cut off mid-entry in the “Model Compression” section (AMC link is broken/unfinished)
• No clear criteria for what makes the “high-quality” cut; curation process is unstated
• Heavily weighted toward 2018–2019; newer work may lag

Verdict Worth bookmarking if you’re doing literature reviews in NAS or model efficiency, or if you need to quickly find a reference implementation. Skip it if you want critical analysis or summaries—this is pure indexing, not commentary.