Optimization & Theory

Bayesian Learning of Neural Network Architectures AISTATS 2019 Beating SGD Saturation with Tail-Averaging and Minibatching NeurIPS 2019 Beyond Backprop: Online Alternating Minimization with Auxiliary Variables ICML 2019 Beyond BLEU: Training Neural Machine Translation with Semantic Similarity ACL 2019 Beyond Gradient Descent for Regularized Segmentation Losses CVPR 2019 Beyond the Single Neuron Convex Barrier for Neural Network Certification NeurIPS 2019 Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit? CVPR 2019 Calibrated Stochastic Gradient Descent for Convolutional Neural Networks AAAI 2019 Capacity Control of ReLU Neural Networks by Basis-Path Norm AAAI 2019 Cascaded Projection: End-To-End Network Compression and Acceleration CVPR 2019 Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure CVPR 2019 Certified Robustness to Adversarial Word Substitutions EMNLP 2019 Certifying Geometric Robustness of Neural Networks NeurIPS 2019 ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation CVPR 2019 CircConv: A Structured Convolution with Low Complexity AAAI 2019 Circulant Binary Convolutional Networks: Enhancing the Performance of 1-Bit DCNNs With Circulant Back Propagation CVPR 2019 Class-Balanced Loss Based on Effective Number of Samples CVPR 2019 Classifier-Agnostic Saliency Map Extraction AAAI 2019 CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks AAAI 2019 Cold Case: The Lost MNIST Digits NeurIPS 2019 Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training EMNLP 2019 Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback NeurIPS 2019 Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients NeurIPS 2019 Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019 Communication trade-offs for Local-SGD with large step size NeurIPS 2019