Papers with Code 아카이브

Ethics

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

4

평가표

2

보존된 평가 기록

8

코드

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

https://github.com/frenchkrab/is2023-powerset-diarization

Ego4D: Around the World in 3,000 Hours of Egocentric Video

https://github.com/geminiln/ai4gcc

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

https://github.com/hendrycks/test

Aligning AI With Shared Human Values

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

Training Compute-Optimal Large Language Models

https://github.com/kristery/EthicsShaping

A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents

https://github.com/liano3/RAG-fairness

TrustLLM: Trustworthiness in Large Language Models

https://github.com/liweijiang/delphi

Can Machines Learn Morality? The Delphi Experiment

https://github.com/ngatilio/aiethics

Responsible Design Patterns for Machine Learning Pipelines

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

Training Compute-Optimal Large Language Models

https://github.com/nnnnai/ego4d_nlq_2022_1st_place_solution

Ego4D: Around the World in 3,000 Hours of Egocentric Video

https://github.com/ollmer/mmlu

Aligning AI With Shared Human Values

https://github.com/priya22/emotion-granularity

Ethics Sheets for AI Tasks

https://github.com/priya22/emotion-granularity-from-text

Ethics Sheets for AI Tasks

https://github.com/priya22/emotiondynamics

Ethics Sheets for AI Tasks

https://github.com/pyannote/pyannote-audio

Ego4D: Around the World in 3,000 Hours of Egocentric Video

https://github.com/rvlopes/gloria

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

https://github.com/shengyp/fake_news

Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

https://github.com/yeliudev/R2-Tuning

Ego4D: Around the World in 3,000 Hours of Egocentric Video

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