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← Core AI
Artificial Intelligence
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Core AI
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Interpretability
7,318 papers
Papers per year
2003: 1
2006: 1
2007: 1
2008: 1
2009: 1
2010: 5
2012: 2
2013: 10
2014: 7
2015: 14
2016: 27
2017: 84
2018: 196
2019: 395
2020: 488
2021: 771
2022: 823
2023: 954
2024: 1360
2025: 1713
2026: 464
Papers
Scalable Verification of Quantized Neural Networks
AAAI 2021
Anomaly Attribution with Likelihood Compensation
AAAI 2021
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
AAAI 2021
A Hybrid Probabilistic Approach for Table Understanding
AAAI 2021
Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field
AAAI 2021
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
AAAI 2021
Interpretable Actions: Controlling Experts with Understandable Commands
AAAI 2021
Aligning Artificial Neural Networks and Ontologies towards Explainable AI
AAAI 2021
Learning by Fixing: Solving Math Word Problems with Weak Supervision
AAAI 2021
Explaining Neural Matrix Factorization with Gradient Rollback
AAAI 2021
Differentiable Inductive Logic Programming for Structured Examples
AAAI 2021
On the Complexity of Finding Justifications for Collective Decisions
AAAI 2021
MARTA: Leveraging Human Rationales for Explainable Text Classification
AAAI 2021
User Driven Model Adjustment via Boolean Rule Explanations
AAAI 2021
Certifying Top-Down Decision-DNNF Compilers
AAAI 2021
Recursion in Abstract Argumentation is Hard --- On the Complexity of Semantics Based on Weak Admissibility
AAAI 2021
Interpreting Neural Networks as Quantitative Argumentation Frameworks
AAAI 2021
Strong Explanations in Abstract Argumentation
AAAI 2021
On the Tractability of SHAP Explanations
AAAI 2021
Does Explainable Artificial Intelligence Improve Human Decision-Making?
AAAI 2021
The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits
AAAI 2021
TabNet: Attentive Interpretable Tabular Learning
AAAI 2021
Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms
AAAI 2021
HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
AAAI 2021
Learning Prediction Intervals for Model Performance
AAAI 2021
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