← Optimization & Theory
Deep Learning › Optimization & Theory ›

Neural Network Optimization

902 directly classified papers

Papers per year

Papers

Activate or Not: Learning Customized Activation CVPR 2021
Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling AAAI 2021
Scaling-Up Robust Gradient Descent Techniques AAAI 2021
Deep Spiking Neural Network with Neural Oscillation and Spike-Phase Information AAAI 2021
THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation AAAI 2021
Frivolous Units: Wider Networks Are Not Really That Wide AAAI 2021
DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch AAAI 2021
Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse AAAI 2021
SA-BNN: State-Aware Binary Neural Network AAAI 2021
Rethinking Channel Dimensions for Efficient Model Design CVPR 2021
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs CVPR 2021
Prioritized Architecture Sampling With Monto-Carlo Tree Search CVPR 2021
Neural Architecture Search With Random Labels CVPR 2021
Stochastic Whitening Batch Normalization CVPR 2021
Layerwise Optimization by Gradient Decomposition for Continual Learning CVPR 2021
How Does Topology Influence Gradient Propagation and Model Performance of Deep Networks With DenseNet-Type Skip Connections? CVPR 2021
Time Adaptive Recurrent Neural Network CVPR 2021
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling CVPR 2021
Optimizing Deeper Transformers on Small Datasets ACL 2021
LeeBERT: Learned Early Exit for BERT with cross-level optimization ACL 2021
Scale-Aware Automatic Augmentation for Object Detection CVPR 2021
Fast and Accurate Model Scaling CVPR 2021
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization NIPS 2021
Tensor Normal Training for Deep Learning Models NIPS 2021
Label Noise SGD Provably Prefers Flat Global Minimizers NIPS 2021