Code and written solutions of the assignments of the Stanford CS224N: Natural Language Processing with Deep Learning course from winter 2022/2023

cppad+Ipopt demo以及OSQP demo程序

C++ implementation of the SQP algorithm SOLNP, utilizing Lagrangian Relaxation to handle both Inequality and Equality constraint functions. Good for solving constrained objective functions on conve…

Python GOSOLNP implementation based on pysolnp. This algorithm solves Global Optimization problems with optional equality and/or inequality constraints.

An implementation of the algorithm proposed in 'A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent', Ben London 2018

[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation

SOLNP+: A derivative-free optimization software

Measuring generalization properties of graph neural networks

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

Transformer: PyTorch Implementation of "Attention Is All You Need"

Code for Which Tasks Should Be Learned Together in Multi-task Learning?

Google Research

Taskonomy: Disentangling Task Transfer Learning [Best Paper, CVPR2018]

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

Must-read papers on graph neural networks (GNN)

2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.

A PyTorch Library for Multi-Task Learning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

深度学习入门教程, 优秀文章, Deep Learning Tutorial

This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.

GNN综述阅读报告

计算机自学指南

联邦学习

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy