Optimization & Theory

Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data AAAI 2019 Deep Neural Networks Learn Non-Smooth Functions Effectively AISTATS 2019 Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex AISTATS 2019 Deep ReLU Networks Have Surprisingly Few Activation Patterns NeurIPS 2019 Deep Rigid Instance Scene Flow CVPR 2019 Deep Scale-spaces: Equivariance Over Scale NeurIPS 2019 DeepView: View Synthesis With Learned Gradient Descent CVPR 2019 Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks CVPR 2019 Demystifying Black-box Models with Symbolic Metamodels NeurIPS 2019 Depth Coefficients for Depth Completion CVPR 2019 Depth Separations in Neural Networks: What is Actually Being Separated? COLT 2019 Depthwise Convolution Is All You Need for Learning Multiple Visual Domains AAAI 2019 Detecting Overfitting of Deep Generative Networks via Latent Recovery CVPR 2019 DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation NeurIPS 2019 DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation CVPR 2019 Differentiable Convex Optimization Layers NeurIPS 2019 Differentiable Ranking and Sorting using Optimal Transport NeurIPS 2019 Dimension-Free Bounds for Low-Precision Training NeurIPS 2019 Distributed Low-rank Matrix Factorization With Exact Consensus NeurIPS 2019 Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study ACL 2019 Douglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution CVPR 2019 Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment ACL 2019 DVC: An End-To-End Deep Video Compression Framework CVPR 2019 Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function AISTATS 2019 Dynamic Recursive Neural Network CVPR 2019