Theory

Interpreting and Evaluating Neural Network Robustness IJCAI 2019 Interpreting CNNs via Decision Trees CVPR 2019 Invariance and identifiability issues for word embeddings NeurIPS 2019 Is Attention Interpretable? ACL 2019 Is your function low dimensional? COLT 2019 Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces JMLR 2019 Jointly Measuring Diversity and Quality in Text Generation Models NAACL 2019 Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation EMNLP 2019 Jumping Manifolds: Geometry Aware Dense Non-Rigid Structure From Motion CVPR 2019 Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods NeurIPS 2019 Kernel quadrature with DPPs NeurIPS 2019 Kernels for Sequentially Ordered Data JMLR 2019 Kernel Stein Tests for Multiple Model Comparison NeurIPS 2019 Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications CVPR 2019 Large Scale Markov Decision Processes with Changing Rewards NeurIPS 2019 Large Scale Structure of Neural Network Loss Landscapes NeurIPS 2019 Learnability of Solutions to Conjunctive Queries JMLR 2019 Learning and Inference for Structured Prediction: A Unifying Perspective IJCAI 2019 Learning Dependency Structures for Weak Supervision Models ICML 2019 Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning NeurIPS 2019 Learning from Bad Data via Generation NeurIPS 2019 Learning Optimal Strategies to Commit To AAAI 2019 Learning Reward Machines for Partially Observable Reinforcement Learning NeurIPS 2019 Learning Set Functions with Limited Complementarity AAAI 2019 Learning Stable Deep Dynamics Models NeurIPS 2019