Optimization & Theory

Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks ICLR 2021 Multiplicative Noise and Heavy Tails in Stochastic Optimization ICML 2021 Multi-split Reversible Transformers Can Enhance Neural Machine Translation EACL 2021 MVFNet: Multi-View Fusion Network for Efficient Video Recognition AAAI 2021 NAS-Bench-x11 and the Power of Learning Curves NeurIPS 2021 Near Lossless Transfer Learning for Spiking Neural Networks AAAI 2021 NetAdaptV2: Efficient Neural Architecture Search With Fast Super-Network Training and Architecture Optimization CVPR 2021 Network Pruning That Matters: A Case Study on Retraining Variants ICLR 2021 Network Pruning via Performance Maximization CVPR 2021 Network Quantization With Element-Wise Gradient Scaling CVPR 2021 Neural Architecture Search as Sparse Supernet AAAI 2021 Neural Architecture Search With Random Labels CVPR 2021 Neural Camera Simulators CVPR 2021 Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D Shapes CVPR 2021 Neural gradients are near-lognormal: improved quantized and sparse training ICLR 2021 Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation CVPR 2021 Neural Response Interpretation Through the Lens of Critical Pathways CVPR 2021 Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks CVPR 2021 Newton Method over Networks is Fast up to the Statistical Precision ICML 2021 Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks NeurIPS 2021 Noise against noise: stochastic label noise helps combat inherent label noise ICLR 2021 Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling AAAI 2021 Nondeterminism and Instability in Neural Network Optimization ICML 2021 Nonsmooth Implicit Differentiation for Machine-Learning and Optimization NeurIPS 2021 Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks AAAI 2021