Optimization & Theory

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum ICLR 2020 SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization ACL 2020 SMYRF - Efficient Attention using Asymmetric Clustering NeurIPS 2020 Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning AAAI 2020 Soft Threshold Ternary Networks IJCAI 2020 Soft Threshold Weight Reparameterization for Learnable Sparsity ICML 2020 SPAN: A Stochastic Projected Approximate Newton Method AAAI 2020 Sparse Weight Activation Training NeurIPS 2020 Sparsity-Inducing Binarized Neural Networks AAAI 2020 Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error IJCAI 2020 Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks NeurIPS 2020 SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking AAAI 2020 SqueezeBERT: What can computer vision teach NLP about efficient neural networks? EMNLP 2020 SSRNet: Scalable 3D Surface Reconstruction Network CVPR 2020 Stable behaviour of infinitely wide deep neural networks AISTATS 2020 STEER : Simple Temporal Regularization For Neural ODE NeurIPS 2020 Stochastic Approximate Gradient Descent via the Langevin Algorithm AAAI 2020 Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization ICML 2020 Stochastic Loss Function AAAI 2020 Stochastic Optimization with Laggard Data Pipelines NeurIPS 2020 Stolen Probability: A Structural Weakness of Neural Language Models ACL 2020 Streaming Batch Gradient Tracking for Neural Network Training (Student Abstract) AAAI 2020 Strip Pooling: Rethinking Spatial Pooling for Scene Parsing CVPR 2020 Stronger and Faster Wasserstein Adversarial Attacks ICML 2020 Structured Compression by Weight Encryption for Unstructured Pruning and Quantization CVPR 2020