https://github.com/agri-hub/adc
공식 코드Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Papers with Code 아카이브
Interpretable Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 1
- 보존된 평가 기록
- 2
The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.
Source: Assessing the Local Interpretability of Machine Learning Models
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aildnont/HIFIS-model
공식 코드Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness
https://github.com/albermax/innvestigate
공식 코드https://github.com/alessiopittiglio/mm-argfallacy
공식 코드Leveraging Context for Multimodal Fallacy Classification in Political Debates
https://github.com/alibaba-nlp/key-point-analysis
공식 코드Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
https://github.com/aminhn/MPP
공식 코드Constraint-based Sequential Pattern Mining with Decision Diagrams
https://github.com/aminhn/bdtrie
공식 코드Memory-Efficient Sequential Pattern Mining with Hybrid Tries
https://github.com/anaryegen/few_shot_argument_mining
공식 코드Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques
https://github.com/anasishfaque/voltavision
공식 코드VoltaVision: A Transfer Learning model for electronic component classification
https://github.com/andron00e/sparsecbm
공식 코드Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
https://github.com/andruekonst/egbm-gam
공식 코드Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
https://github.com/ankurtaly/Attributions
공식 코드https://github.com/annguy/customer-sentiment-monitor
공식 코드System Design for a Data-driven and Explainable Customer Sentiment Monitor
https://github.com/anonymous-2491/drop-clause-interpretable-tm
공식 코드Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
https://github.com/apairofbigwings/cross-lingual-opinion-mining
공식 코드A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
https://github.com/areejokaili/topic_reranking
공식 코드Re-Ranking Words to Improve Interpretability of Automatically Generated Topics
https://github.com/arvind-balaji/debate-cards
공식 코드DebateSum: A large-scale argument mining and summarization dataset
https://github.com/asmadotgh/dissect
공식 코드DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
https://github.com/avrambardas/interpretable-unsupervised-learning
공식 코드https://github.com/bassam-motional/Reasonable-Crowd
공식 코드The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
https://github.com/bigdata-ustc/NeuralCD
공식 코드Neural Cognitive Diagnosis for Intelligent Education Systems
https://github.com/bigdata-ustc/Zero-1-to-3
공식 코드https://github.com/bigdata-ustc/coral
공식 코드Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
https://github.com/bigdata-ustc/educat
공식 코드Survey of Computerized Adaptive Testing: A Machine Learning Perspective
https://github.com/bimalb58/logical-tsetlin-machine-robustness
공식 코드https://github.com/bimk/intelligent-education
공식 코드DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
https://github.com/bio-hpc/sibila
공식 코드https://github.com/bips-hb/cfi_mixeddata
공식 코드https://github.com/bobxwu/cfdtm
공식 코드https://github.com/bobxwu/topmost
공식 코드https://github.com/boschresearch/exekglib
공식 코드ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
https://github.com/boschresearch/hee_dataset
공식 코드Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
https://github.com/boschresearch/trajectory_games_learning
공식 코드Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
https://github.com/bxshi/DiscourseVisualization
공식 코드https://github.com/ceciliasalvatore/sfcca
공식 코드Supervised Feature Compression based on Counterfactual Analysis
https://github.com/cedricdeboom/RepresentationLearning
공식 코드Representation learning for very short texts using weighted word embedding aggregation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.