Papers with Code 아카이브

Physical Intuition

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평가표

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보존된 평가 기록

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코드

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

https://github.com/DongYang26/PI-DRME

공식 코드

Physics-Inspired Distributed Radio Map Estimation

https://github.com/WenboGong/MetaSGMCMC

공식 코드

Meta-Learning for Stochastic Gradient MCMC

https://github.com/facebook/UETorch

공식 코드

Learning Physical Intuition of Block Towers by Example

https://github.com/hzzzzzhappy/mc4ad

공식 코드

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection

https://github.com/juliagni/geometricmachinelearning.jl

공식 코드

Generalizing Adam to Manifolds for Efficiently Training Transformers

https://github.com/lamm-mit/mechagents

공식 코드

MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

https://github.com/mlds-nus/deeplearningcustomthermodynamics

공식 코드

Constructing Custom Thermodynamics Using Deep Learning

https://github.com/ogroth/shapestacks

공식 코드

ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

https://github.com/wyu54/many-body-force-infer

공식 코드

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

https://github.com/xqh19970407/InvDesFlow

공식 코드

InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors

https://github.com/yabozkurt/gmvae

공식 코드

Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders

https://github.com/allenai/dolma

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

https://github.com/bramiozo/PubScience

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

https://github.com/facebookarchive/uetorch

Learning Physical Intuition of Block Towers by Example

https://github.com/hammer-wang/oml-ppo/blob/master/README.md

Automated Optical Multi-layer Design via Deep Reinforcement Learning

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/nkluge-correa/teenytinyllama

Training Compute-Optimal Large Language Models

https://github.com/pmh47/textured-mesh-gen

Leveraging 2D Data to Learn Textured 3D Mesh Generation

https://github.com/rvlopes/gloria

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.