conftrace_
← Core AI
Artificial Intelligence › Core AI ›

Interpretability

7,318 papers

Papers per year

Papers

Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems AAAI 2021
Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability AAAI 2021
Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond AAAI 2021
Multidimensional Uncertainty-Aware Evidential Neural Networks AAAI 2021
Accurate and Robust Feature Importance Estimation under Distribution Shifts AAAI 2021
Neural Utility Functions AAAI 2021
Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions AAAI 2021
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors AAAI 2021
Interpretable Sequence Classification via Discrete Optimization AAAI 2021
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain AAAI 2021
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods AAAI 2021
Expected Eligibility Traces AAAI 2021
Self-Supervised Attention-Aware Reinforcement Learning AAAI 2021
Interpreting Multivariate Shapley Interactions in DNNs AAAI 2021
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach AAAI 2021
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments AAAI 2021
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example AAAI 2021
FIMAP: Feature Importance by Minimal Adversarial Perturbation AAAI 2021
A Unified Taylor Framework for Revisiting Attribution Methods AAAI 2021
On the Verification of Neural ODEs with Stochastic Guarantees AAAI 2021
Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization AAAI 2021
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning AAAI 2021
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations AAAI 2021
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation AAAI 2021
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors AAAI 2021