DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.
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Updated
Jul 7, 2025 - TypeScript
DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.
An SDK for multi-agent collaborative perception.
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)
a collaborative collection of interview questions collected from both sides of the game: Interviewer(s) and Interviewee.
Secure collaborative training and inference for XGBoost.
Official PyTorch Implementation for DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis - CVPR 2022
NEBULA: A Platform for Decentralized Federated Learning
Official implementation of our work "Collaborative Fairness in Federated Learning."
Learning to Walk with Dual Agents for Knowledge Graph Reasoning (AAAI'22)
[ICLR'25] Official Implementation of STAMP: Scalable Task And Model-agnostic Collaborative Perception
Collective Knowledge repository with actions to unify the access to different predictive analytics engines (scipy, R, DNN) from software, command line and web-services via CK JSON API:
Expert Systems With Applications - Camera clustering for scalable stream-based active distillation
Fedstellar: A Platform for Decentralized Federated Learning
Official implementation of FedGAT: Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction (https://arxiv.org/abs/2502.04521)
A Collaborative Learning platform: cataloging and remixing of Open Educational Resources (OER), e-mentoring and e-tutoring, Learning Analytics (LA), and more.
Simple and efficient way to learn a new programming language.
[AAAI-24] TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
Human-Agent Collaborative Deep Reinforcement Learning
Multi-agent reinforcement learning framework for Unity environments. Implements MAPPO, MASAC, MATD3, and MADDPG with comprehensive evaluation tools. Features sample-efficient training, competitive analysis, and pre-trained models achieving great performance in Tennis and Soccer environments.
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