Papers with Code 아카이브

Bias Detection

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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/ychika/dpsw

공식 코드

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation

https://github.com/yichaocai1/crossmodal_mislaignment

공식 코드

On the Value of Cross-Modal Misalignment in Multimodal Representation Learning

https://github.com/ying531/jomi-paper

공식 코드

Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage

https://github.com/yoandinkov/interspeech-2019

공식 코드

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

https://github.com/yuqianruc/drss-code

공식 코드

Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap

https://github.com/yxwan123/biasasker

공식 코드

BiasAsker: Measuring the Bias in Conversational AI System

https://github.com/zwx-open/evos-inr

공식 코드

EVOS: Efficient Implicit Neural Training via EVOlutionary Selector

https://github.com/zyxnlp/diva

공식 코드

Causal Inference from Text: Unveiling Interactions between Variables

https://gitlab.com/shinkaiika/noise-level-causal-identification-additive-noise-models

공식 코드

The Effect of Noise Level on Causal Identification with Additive Noise Models

https://github.com/ML-KULeuven/SAR-PU

Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data

https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset

PanNuke Dataset Extension, Insights and Baselines

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/escm2

ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/multitask/esmm

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

https://github.com/Shantanu48114860/Deep-Counterfactual-Networks-with-Propensity-Dropout

Deep Counterfactual Networks with Propensity-Dropout

https://github.com/UlionTse/mlgb

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

https://github.com/aaparna/UNet-Image-Segmentation

PanNuke Dataset Extension, Insights and Baselines

https://github.com/alexjfoote/reetoolbox

PanNuke Dataset Extension, Insights and Baselines

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/esmm.py

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

https://github.com/bernardodionisi/differences

Difference-in-Differences with a Continuous Treatment

https://github.com/cran/tree.interpreter

A Debiased MDI Feature Importance Measure for Random Forests

https://github.com/cxjdavin/adaptivity-complexity-for-causal-graph-discovery

Active Structure Learning of Causal DAGs via Directed Clique Tree

https://github.com/cxjdavin/subset-verification-and-search-algorithms-for-causal-dags

Active Structure Learning of Causal DAGs via Directed Clique Tree

https://github.com/cxjdavin/verification-and-search-algorithms-for-causal-dags

Active Structure Learning of Causal DAGs via Directed Clique Tree

https://github.com/davidinouye/automated-dependence-plots

Automated Dependence Plots

https://github.com/ericstrobl/CCI

A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias

https://github.com/ghzhang233/Leakage-Neutral-Learning-for-QuoraQP

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

https://github.com/groovychoons/GlobalBias

Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases

https://github.com/kaiseem/pointnu-net

PanNuke Dataset Extension, Insights and Baselines

https://github.com/kamruzzaman15/prompting-techniques-to-reduce-social-bias-in-llms

Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models

https://github.com/kyunghoonban/rdid

Robust Difference-in-differences Models

https://github.com/lhaof/cgt

PanNuke Dataset Extension, Insights and Baselines

https://github.com/lylin0/inditag

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators

https://github.com/nalzok/tree.interpreter

A Debiased MDI Feature Importance Measure for Random Forests

https://github.com/ngatilio/aiethics

Never trust, always verify : a roadmap for Trustworthy AI?

https://github.com/okunator/Dippa

PanNuke Dataset Extension, Insights and Baselines

https://github.com/rfgong/narita

Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules

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