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Starred repositories
Official Implementation of "AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing"
Official implementation of the paper: "ZClip: Adaptive Spike Mitigation for LLM Pre-Training".
Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference
Two-party Privacy-preserving Neural Network Training using Split Learning and Homomorphic Encryption (CKKS Scheme)
A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).
[NDSS 2025] "CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models"
Official code for "DCT-CryptoNets: Scaling Private Inference in the Frequency Domain" [ICLR 2025]
[NeurIPS'24] Official implement of "PrivCirNet: Efficient Private Inference via Block Circulant Transformation"
[NeurIPS'24] Official implement of "PrivCirNet: Efficient Private Inference via Block Circulant Transformation"
The first 3PC framework for Diffusion Model sampling.
Rational Activation Functions - Replacing Padé Activation Units
This repository includes the MATLAB codes of the Bisection method for rational and generalised rational approximations.
The homepage of OneBit model quantization framework.
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
Awesome LLM compression research papers and tools.
[NeurIPS 2022] A Fast Post-Training Pruning Framework for Transformers
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A fast, crude approximation of the gelu activation function using piecewise polynomials
Julia package for approximation by rational functions
Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network
Rational Function Approximation with Positive Denominators
Implementation of Remez Algorithm for Polynomial and Rational Function Approximation.
Remez algorithm for computing minimax polynomial approximations
Interpolation and function approximation with JAX