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Harbin Institude of Technology, Shenzhen
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OpenICL is an open-source framework to facilitate research, development, and prototyping of in-context learning.
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
Pytorch implementation of various Knowledge Distillation (KD) methods.
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content…
Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.
Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)
A curated list of awesome Machine Learning frameworks, libraries and software.
Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.
深度学习系统笔记,包含深度学习数学基础知识、神经网络基础部件详解、深度学习炼丹策略、模型压缩算法详解、和大模型基础和推理性能优化分析。
《开源大模型食用指南》基于Linux环境快速部署开源大模型,更适合中国宝宝的部署教程
An implementation of online data mixing for the Pile dataset, based on the GPT-NeoX library.
A curated list of resources for Learning with Noisy Labels
A curated list of papers and resources about the distribution shift in machine learning.
A curated list of awesome papers about calibration
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc
ImageNet Testbed, associated with the paper "Measuring Robustness to Natural Distribution Shifts in Image Classification."
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
The Official Repository for "Generalized OOD Detection: A Survey"
💎 The Principles of OOD (SOLID) based on Uncle Bob articles.
Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source librarie…
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
A collection of AWESOME things about domian adaptation