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ashleyxly
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Learning Fraud Detection from research papers and industry applications.
utilities for decoding deep representations (like sentence embeddings) back to text
This is the course resources repository to learn tugraph projects.
Language server protocol implementation for VSCode. This allows implementing language services in JS/TS running on node.js
SecMML (Queqiao): Secure MPC (multi-party computation) Machine Learning Framework.
A framework for Privacy Preserving Machine Learning
MiniTracker: Large-Scale Sensitive Information Tracking in Mini Apps.
Official Code for CVPR 2024 paper: Permutation Equivariance of Transformers and Its Applications.
zkml-community / awesome-zkml
Forked from worldcoin/awesome-zkmlAggregator for amazing ZKML resources
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
Training PyTorch models with differential privacy
A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
A curated list of multi party computation resources and links.
This is a PyTorch implementation of the paperViP A Differentially Private Foundation Model for Computer Vision
[ACL 2024] The official GitHub repo for the paper "The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation"
open source code for NeurIPS 2024 paper
Code for paper "SrcMarker: Dual-Channel Source Code Watermarking via Scalable Code Transformations" (IEEE S&P 2024)
High accuracy captcha solver for SJTU Jaccount login page using SVM and ResNet.
High accuracy captcha solver for SJTU Jaccount login page using SVM and ResNet.
A codebase that makes differentially private training of transformers easy.
A pure Rust PLONK implementation using arkworks as a backend.
The implementation of "Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases"
A Survey of Deep Learning Models for Structural Code Understanding