Optimization & Theory

Comparing Sample-Wise Learnability across Deep Neural Network Models AAAI 2019 Complex Unitary Recurrent Neural Networks Using Scaled Cayley Transform AAAI 2019 Composite Binary Decomposition Networks AAAI 2019 Compressing Convolutional Neural Networks via Factorized Convolutional Filters CVPR 2019 Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition AAAI 2019 CondConv: Conditionally Parameterized Convolutions for Efficient Inference NeurIPS 2019 Conditional Independence Testing using Generative Adversarial Networks NeurIPS 2019 Constrained deep neural network architecture search for IoT devices accounting for hardware calibration NeurIPS 2019 Continual and Multi-Task Architecture Search ACL 2019 Convergence of Gradient Descent on Separable Data AISTATS 2019 Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation ICLR 2019 Coresets for Clustering with Fairness Constraints NeurIPS 2019 Cross Domain Model Compression by Structurally Weight Sharing CVPR 2019 Customizable Architecture Search for Semantic Segmentation CVPR 2019 Data-Distortion Guided Self-Distillation for Deep Neural Networks AAAI 2019 Data-driven Estimation of Sinusoid Frequencies NeurIPS 2019 Data-Driven Neuron Allocation for Scale Aggregation Networks CVPR 2019 Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation CVPR 2019 Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask NeurIPS 2019 Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses CVPR 2019 Deep Dominance - How to Properly Compare Deep Neural Models ACL 2019 Deep Equilibrium Models NeurIPS 2019 Deeper and Wider Siamese Networks for Real-Time Visual Tracking CVPR 2019 Deep Generalized Method of Moments for Instrumental Variable Analysis NeurIPS 2019 Deep Layers as Stochastic Solvers ICLR 2019