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Learning-Rate Annealing Methods for Deep Neural Networks

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01231-1_39openalex-snapshot
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_38openalex-snapshot
Identity Mappings in Deep Residual Networks
inferredcites →https://doi.org/10.1007/978-3-7908-2604-3_16openalex-snapshot
Large-Scale Machine Learning with Stochastic Gradient Descent
inferredcites →https://doi.org/10.1007/s10489-021-02973-4openalex-snapshot
Graph convolutional networks: analysis, improvements and results
inferredcites →https://doi.org/10.1007/s10994-006-8365-9openalex-snapshot
Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1088/1742-5468/ab39d9openalex-snapshot
Entropy-SGD: biasing gradient descent into wide valleys*
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/bigmm.2017.36openalex-snapshot
T-LRA: Trend-Based Learning Rate Annealing for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00065openalex-snapshot
Bag of Tricks for Image Classification with Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/icassp.2017.7952131openalex-snapshot
A comparison of Deep Learning methods for environmental sound detection
inferredcites →https://doi.org/10.1109/iccv.2015.312openalex-snapshot
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
inferredcites →https://doi.org/10.1109/iccv.2015.316openalex-snapshot
FlowNet: Learning Optical Flow with Convolutional Networks
inferredcites →https://doi.org/10.1109/icra.2017.7989324openalex-snapshot
Deep visual foresight for planning robot motion
inferredcites →https://doi.org/10.1109/ita.2018.8503173openalex-snapshot
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization
inferredcites →https://doi.org/10.1109/taslp.2017.2778423openalex-snapshot
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
inferredcites →https://doi.org/10.1109/tip.2017.2713099openalex-snapshot
Deep Convolutional Neural Network for Inverse Problems in Imaging
inferredcites →https://doi.org/10.1109/tip.2018.2794218openalex-snapshot
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
inferredcites →https://doi.org/10.1109/tpami.2017.2691321openalex-snapshot
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
inferredcites →https://doi.org/10.1109/wacv.2017.58openalex-snapshot
Cyclical Learning Rates for Training Neural Networks
inferredcites →https://doi.org/10.1137/16m1080173openalex-snapshot
Optimization Methods for Large-Scale Machine Learning
inferredcites →https://doi.org/10.1145/1015330.1015332openalex-snapshot
Solving large scale linear prediction problems using stochastic gradient descent algorithms
inferredcites →https://doi.org/10.1145/2783258.2783273openalex-snapshot
Collaborative Deep Learning for Recommender Systems
inferredcites →https://doi.org/10.1145/2988450.2988454openalex-snapshot
Wide & Deep Learning for Recommender Systems
inferredcites →https://doi.org/10.1145/3306240openalex-snapshot
How Deep Features Have Improved Event Recognition in Multimedia
inferredcites →https://doi.org/10.1162/neco.1997.9.1.1openalex-snapshot
Flat Minima
inferredcites →https://doi.org/10.1214/aoms/1177729586openalex-snapshot
A Stochastic Approximation Method
inferredcites →https://doi.org/10.1609/aaai.v31i1.10940openalex-snapshot
Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization
inferredcites →https://doi.org/10.3390/a14060186openalex-snapshot
Combining Optimization Methods Using an Adaptive Meta Optimizer
inferredcites →https://doi.org/10.48550/arxiv.1202.6258openalex-snapshot
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
inferredcites →https://doi.org/10.48550/arxiv.1206.1106openalex-snapshot
No More Pesky Learning Rates
inferredcites →https://doi.org/10.48550/arxiv.1212.5701openalex-snapshot
ADADELTA: An Adaptive Learning Rate Method
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1509.01240openalex-snapshot
Train faster, generalize better: Stability of stochastic gradient descent
inferredcites →https://doi.org/10.48550/arxiv.1603.05643openalex-snapshot
Variance Reduction for Faster Non-Convex Optimization
inferredcites →https://doi.org/10.48550/arxiv.1608.03983openalex-snapshot
SGDR: Stochastic Gradient Descent with Warm Restarts
inferredcites →https://doi.org/10.48550/arxiv.1705.08292openalex-snapshot
The Marginal Value of Adaptive Gradient Methods in Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1706.02677openalex-snapshot
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
inferredcites →https://doi.org/10.48550/arxiv.1708.07747openalex-snapshot
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1711.00489openalex-snapshot
Don't Decay the Learning Rate, Increase the Batch Size
inferredcites →https://doi.org/10.48550/arxiv.1802.04791openalex-snapshot
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
inferredcites →https://doi.org/10.48550/arxiv.1802.05431openalex-snapshot
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
inferredcites →https://doi.org/10.48550/arxiv.1802.08770openalex-snapshot
A Walk with SGD
inferredcites →https://doi.org/10.48550/arxiv.1805.10367openalex-snapshot
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
inferredcites →https://doi.org/10.48550/arxiv.1806.11027openalex-snapshot
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
inferredcites →https://doi.org/10.48550/arxiv.1808.02169openalex-snapshot
Fast Variance Reduction Method with Stochastic Batch Size
inferredcites →https://doi.org/10.48550/arxiv.1907.04595openalex-snapshot
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2103.12623openalex-snapshot
Evolving Learning Rate Optimizers for Deep Neural Networks
inferredcites →https://doi.org/10.57702/e7wwo0zkopenalex-snapshot
Reading digits in natural images with unsupervised feature learning
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images

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