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Graph Neural Network Library for PyTorch
Benchmark datasets, data loaders, and evaluators for graph machine learning
a pytorch implement of mobileNet v2 on cifar10
[ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-…
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
A graph neural network tailored to directed acyclic graphs that outperforms conventional GNNs by leveraging the partial order as strong inductive bias besides other suitable architectural features.
Graphormer is a general-purpose deep learning backbone for molecular modeling.
Code for "Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?" [ICML 2023]
Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)
Recipe for a General, Powerful, Scalable Graph Transformer
Naszilla is a Python library for neural architecture search (NAS)
Official Implementation of Robustifying and Boosting Training-Free Neural Architecture Search
NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.
PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.
this is a STM32F411 development kit for AI and UI development, Cheap, small and powerful.
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision
[ACL 2024] GroundingGPT: Language-Enhanced Multi-modal Grounding Model
TPAMI 2021: NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
Measuring and predicting on-device metrics (latency, power, etc.) of machine learning models