Stars
Learning Efficient Representations for Keyword Spotting with Triplet Loss
Acceptance rates for the major AI conferences
League of Legends esports data ETL pipeline.
Run safety benchmarks against AI models and view detailed reports showing how well they performed.
A Python package to assess and improve fairness of machine learning models.
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods.
AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for…
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Automated deep learning algorithms implemented in PyTorch.
Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation.
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
[JAIR'23] FlexiBERT tool for Transformer design space exploration.
[ACL'22] Training-free Neural Architecture Search for RNNs and Transformers
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.
A curated list of awesome resources combining Transformers with Neural Architecture Search
Differentiable architecture search for convolutional and recurrent networks
Notes on Deep Learning textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
ICML'2022: NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
📊 Simple package for monitoring and control your NVIDIA Jetson [Orin, Xavier, Nano, TX] series
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
Fast, flexible and easy to use probabilistic modelling in Python.
[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Estimation of Distribution algorithms Python package