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Interpretability

499 papers

Papers per year

3
65
64
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Papers

2D-OOB: Attributing Data Contribution Through Joint Valuation Framework NIPS 2024
Stochastic Concept Bottleneck Models NIPS 2024
Dissecting Query-Key Interaction in Vision Transformers NIPS 2024
Selective Explanations NIPS 2024
Localizing Memorization in SSL Vision Encoders NIPS 2024
Listenable Maps for Zero-Shot Audio Classifiers NIPS 2024
Training Data Attribution via Approximate Unrolling NIPS 2024
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics NIPS 2024
Relational Concept Bottleneck Models NIPS 2024
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks NIPS 2024
FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors NIPS 2024
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models NIPS 2024
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory NIPS 2024
Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection NIPS 2024
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? NIPS 2024
The Intelligible and Effective Graph Neural Additive Network NIPS 2024
Hypothesis Testing the Circuit Hypothesis in LLMs NIPS 2024
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment AAAI 2024
Empowering CAM-Based Methods with Capability to Generate Fine-Grained and High-Faithfulness Explanations AAAI 2024
Exploring Diverse Representations for Open Set Recognition AAAI 2024
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning AAAI 2024
A General Theoretical Framework for Learning Smallest Interpretable Models AAAI 2024
Accelerating the Global Aggregation of Local Explanations AAAI 2024
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods AAAI 2024
Generative Model for Decision Trees AAAI 2024