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Interpretability

349 directly classified papers

Papers per year

Papers

On the Universal Truthfulness Hyperplane Inside LLMs EMNLP 2024
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs EMNLP 2024
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis NIPS 2024
An L* Algorithm for Deterministic Weighted Regular Languages EMNLP 2024
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning EMNLP 2024
Does Large Language Model Contain Task-Specific Neurons? EMNLP 2024
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales 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
Accelerating the Global Aggregation of Local Explanations AAAI 2024
Generative Model for Decision Trees AAAI 2024
Latent Concept-based Explanation of NLP Models EMNLP 2024
Robust Stochastic Graph Generator for Counterfactual Explanations AAAI 2024
Backward Lens: Projecting Language Model Gradients into the Vocabulary Space EMNLP 2024
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference AAAI 2024
G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint) AAAI 2024
Interactive Mars Image Content-Based Search with Interpretable Machine Learning AAAI 2024
Cluster-Norm for Unsupervised Probing of Knowledge EMNLP 2024
LACIE: Listener-Aware Finetuning for Calibration in Large Language Models NIPS 2024
Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection NIPS 2024
Learning Bottleneck Concepts in Image Classification CVPR 2023
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations CVPR 2023
Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability CVPR 2023