Optimization & Theory

Learning Image and Video Compression Through Spatial-Temporal Energy Compaction CVPR 2019 Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract) IJCAI 2019 Learning Not to Learn: Training Deep Neural Networks With Biased Data CVPR 2019 Learning One-hidden-layer Neural Networks under General Input Distributions AISTATS 2019 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization ACL 2019 Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss CVPR 2019 Learning to Rank for Plausible Plausibility ACL 2019 Learning to Sample CVPR 2019 Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks NeurIPS 2019 Lifted Proximal Operator Machines AAAI 2019 Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks ACL 2019 Limitations of the empirical Fisher approximation for natural gradient descent NeurIPS 2019 LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition NeurIPS 2019 Local SGD Converges Fast and Communicates Little ICLR 2019 Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization NeurIPS 2019 Logistic Regression on Homomorphic Encrypted Data at Scale AAAI 2019 MAGSAC: Marginalizing Sample Consensus CVPR 2019 Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019 Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets AAAI 2019 Massively scalable Sinkhorn distances via the Nyström method NeurIPS 2019 MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors CVPR 2019 Max-Sliced Wasserstein Distance and Its Use for GANs CVPR 2019 MBS: Macroblock Scaling for CNN Model Reduction CVPR 2019 Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit COLT 2019