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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zilin-huang/PE-RLHF

공식 코드

Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Driving

https://github.com/zongliny/inductive_reasoning

공식 코드

Language Models as Inductive Reasoners

https://gitlab.com/sazwar/sub-goal-construction

공식 코드

Exploring Inevitable Waypoints for Unsolvability Explanation in Hybrid Planning Problems

https://gitlab.com/soumendu041/laser

공식 코드

A new approach to locally adaptive polynomial regression

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/vanillanet

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/ACA-Lab-SJTU/approximate-computing

AXNet: ApproXimate computing using an end-to-end trainable neural network

https://github.com/BouchardLab/pylearn2

Pylearn2: a machine learning research library

https://github.com/ChaoningZhang/Dual-temperature

Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness

https://github.com/DiT-3D/DiT-3D

DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation

https://github.com/ForrestPi/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/FutureXiang/edm2

Analyzing and Improving the Training Dynamics of Diffusion Models

https://github.com/GXYM/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/HowieHwong/TrustLLM

I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench

https://github.com/IntelLabs/distiller

Neural Network Distiller: A Python Package For DNN Compression Research

https://github.com/JennyVanessa/Paddle-GHM

Gradient Harmonized Single-stage Detector

https://github.com/MindCode-4/code-5/tree/main/van

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/MindSpore-paper-code-2/code3/tree/main/vanillanet

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/MindSpore-paper-code-2/code399/tree/main/Transformer

A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation

https://github.com/MindSpore-paper-code-3/code4/tree/main/HDR-Transformer

A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation

https://github.com/MollySQuinn/MollySQuinn.github.io

The Unexpected Unexpected and the Expected Unexpected: How People's Conception of the Unexpected is Not That Unexpected

https://github.com/NVlabs/NViT

Global Vision Transformer Pruning with Hessian-Aware Saliency

https://github.com/OZA15015/pruning

Neural Network Distiller: A Python Package For DNN Compression Research

https://github.com/PKD667/political-alignement

Propaganda is all you need

https://github.com/PaddlePaddle/DeepSpeech

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit

https://github.com/PengZhenghao/AXNet

AXNet: ApproXimate computing using an end-to-end trainable neural network

https://github.com/RasaHQ/rasa

Rasa: Open Source Language Understanding and Dialogue Management

https://github.com/Westlake-AI/openmixup

VanillaNet: the Power of Minimalism in Deep Learning

https://github.com/YichengDWu/tinyedm

Analyzing and Improving the Training Dynamics of Diffusion Models

https://github.com/ajgates42/clusim

Element-centric clustering comparison unifies overlaps and hierarchy

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/amlalejini/GECCO-2018-Evolving-Event-driven-Programs-with-SignalGP

Evolving Event-driven Programs with SignalGP

https://github.com/asleepzzz/freeze_miopen

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/asleepzzz/nhwc_miopen

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/asleepzzz/nhwc_shuffle

MIOpen: An Open Source Library For Deep Learning Primitives

https://github.com/awslabs/autogluon

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.