conftrace_
← Core AI
Artificial Intelligence › Core AI ›

Interpretability

7,318 papers

Papers per year

Papers

GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer ACL 2022
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data ACL 2022
Monte Carlo Tree Search for Interpreting Stress in Natural Language ACL 2022
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection ACL 2022
Analyzing Gender Representation in Multilingual Models ACL 2022
Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations ACL 2022
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations ACL 2022
On the Complementarity of Images and Text for the Expression of Emotions in Social Media ACL 2022
“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words ACL 2022
Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives ACL 2022
Out of Distribution Detection via Neural Network Anchoring ACML 2022
Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs ACML 2022
3D Manifold Topology Based Medical Image Data Augmentation ACML 2022
BINAS: Bilinear Interpretable Neural Architecture Search ACML 2022
On the Interpretability of Attention Networks ACML 2022
Auto-Physics-Encoder: Using Physics-Informed Latent Layer Two-Way Physics Flow for Monitoring Systems with Unobservability ACML 2022
Cross-Loss Influence Functions to Explain Deep Network Representations AISTATS 2022
Survival regression with proper scoring rules and monotonic neural networks AISTATS 2022
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations AISTATS 2022
Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees AISTATS 2022
Accurate Shapley Values for explaining tree-based models AISTATS 2022
Measuring the robustness of Gaussian processes to kernel choice AISTATS 2022
LIMESegment: Meaningful, Realistic Time Series Explanations AISTATS 2022
Mitigating Bias in Calibration Error Estimation AISTATS 2022
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks AISTATS 2022