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

https://github.com/natolambert/dynamicslearn

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/naver-ai/jood

Jailbreaking Attack against Multimodal Large Language Model

https://github.com/neelsjain/baseline-defenses

Detecting Language Model Attacks with Perplexity

https://github.com/neillu23/DiffuSE

DiffWave: A Versatile Diffusion Model for Audio Synthesis

https://github.com/neuir/llm-qe

Repetition Improves Language Model Embeddings

https://github.com/neverix/rlhf-trojan-2024-cod

Towards Efficient Exact Optimization of Language Model Alignment

https://github.com/nicoManthey/Mining-Claims-in-UNHCR-reports-A2

Joint RNN Model for Argument Component Boundary Detection

https://github.com/nikhil-krishna/NeurIPS-Reproducibility-Challenge

Fooling Neural Network Interpretations via Adversarial Model Manipulation

https://github.com/nimiew/Grab-Computer-Vision

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/nlpodyssey/rwkv.f90

Accelerating Large Language Model Decoding with Speculative Sampling

https://github.com/northeastsquare/effficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/nvidia-cosmos/cosmos-transfer1

Cosmos World Foundation Model Platform for Physical AI

https://github.com/nvlabs/radio

FeatSharp: Your Vision Model Features, Sharper

https://github.com/nvnhat95/Natural-Language-Inference

A Decomposable Attention Model for Natural Language Inference

https://github.com/obster991/RL-model-based-planning-from-scratch

Learning model-based planning from scratch

https://github.com/oceanypt/A-DEEP-REINFORCED-MODEL-FOR-ABSTRACTIVE-SUMMARIZATION

A Deep Reinforced Model for Abstractive Summarization

https://github.com/ojimenezn/ml-interpretability

A Unified Approach to Interpreting Model Predictions

https://github.com/okasag/OrderedForest

Random Forest Estimation of the Ordered Choice Model

https://github.com/okasag/orf

Random Forest Estimation of the Ordered Choice Model

https://github.com/open-edge-platform/geti

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/open-edge-platform/training_extensions

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/open-finance-lab/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/open-mmlab/mmdetection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/openbmb/debater

Repetition Improves Language Model Embeddings

https://github.com/openbmb/eurus

KTO: Model Alignment as Prospect Theoretic Optimization

https://github.com/openclimatefix/graph_weather

A Foundation Model for the Earth System

https://github.com/openclimatefix/metnet

MetNet: A Neural Weather Model for Precipitation Forecasting

https://github.com/opendfm/chemdfm

ChemDFM: A Large Language Foundation Model for Chemistry

https://github.com/opendilab/DI-engine

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/opendrivelab/uniad

GenAD: Generalized Predictive Model for Autonomous Driving

https://github.com/opengvlab/llama-adapter

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

https://github.com/openseg-group/OCNet.pytorch

SegFix: Model-Agnostic Boundary Refinement for Segmentation

https://github.com/openvla/openvla

OpenVLA: An Open-Source Vision-Language-Action Model

https://github.com/osilab-kaist/komet-benchmark-dataset

MetNet: A Neural Weather Model for Precipitation Forecasting

https://github.com/osmr/imgclsmob

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/otosense/slang

WaveNet: A Generative Model for Raw Audio

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