Papers with Code 아카이브
Bias Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 5
- 보존된 평가 기록
- 19
Bias detection is the task of detecting and measuring racism, sexism and otherwise discriminatory behavior in a model (Source: https://stereoset.mit.edu/)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shenweichen/DeepCTR
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
https://github.com/shenweichen/DeepCTR-Torch
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
https://github.com/tangxyw/RecAlgorithm
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
https://github.com/uitml/nohub
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
https://github.com/vqdang/hover_net
https://github.com/wmkouw/covshift-skewness
Effects of sampling skewness of the importance-weighted risk estimator on model selection
https://github.com/wmkouw/ctrl-iwxval
Robust importance-weighted cross-validation under sample selection bias
https://github.com/wmkouw/libTLDA
Target contrastive pessimistic risk for robust domain adaptation
https://github.com/xinshuli2022/cite
https://github.com/yccm/diffpo
DiffPO: A causal diffusion model for learning distributions of potential outcomes
https://github.com/yujiabao/ls
https://github.com/zalkikar/mlm-bias
StereoSet: Measuring stereotypical bias in pretrained language models
https://github.com/ziqi-li/geoshapley
https://github.com/zwx-open/Symmetric-Power-Transformation-INR
EVOS: Efficient Implicit Neural Training via EVOlutionary Selector
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.