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Neural Network Optimization

903 papers

Papers per year

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9
127
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Papers

Rethinking Channel Dimensions for Efficient Model Design CVPR 2021
Orthogonal Over-Parameterized Training CVPR 2021
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs CVPR 2021
Scale-Aware Automatic Augmentation for Object Detection CVPR 2021
Prioritized Architecture Sampling With Monto-Carlo Tree Search CVPR 2021
Neural Architecture Search With Random Labels CVPR 2021
How Does Topology Influence Gradient Propagation and Model Performance of Deep Networks With DenseNet-Type Skip Connections? CVPR 2021
Stochastic Whitening Batch Normalization CVPR 2021
Layerwise Optimization by Gradient Decomposition for Continual Learning CVPR 2021
Time Adaptive Recurrent Neural Network CVPR 2021
Activate or Not: Learning Customized Activation CVPR 2021
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks CVPR 2021
HR-NAS: Searching Efficient High-Resolution Neural Architectures With Lightweight Transformers CVPR 2021
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling CVPR 2021
Fast and Accurate Model Scaling CVPR 2021
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent EMNLP 2021
MTAdam: Automatic Balancing of Multiple Training Loss Terms EMNLP 2021
Reconsidering the Past: Optimizing Hidden States in Language Models EMNLP 2021
ARCH: Efficient Adversarial Regularized Training with Caching EMNLP 2021
ReconfigISP: Reconfigurable Camera Image Processing Pipeline ICCV 2021
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks ICML 2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling ICML 2021
Bayesian Deep Learning via Subnetwork Inference ICML 2021
Adversarial Robustness Guarantees for Random Deep Neural Networks ICML 2021
On Energy-Based Models with Overparametrized Shallow Neural Networks ICML 2021