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02/17/2022
General Cyclical Training of Neural Networks
This paper describes the principle of "General Cyclical Training" in mac...
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02/16/2022
Cyclical Focal Loss
The cross-entropy softmax loss is the primary loss function used to trai...
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11/18/2020
FROST: Faster and more Robust One-shot Semi-supervised Training
Recent advances in one-shot semi-supervised learning have lowered the ba...
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06/16/2020
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
Reaching the performance of fully supervised learning with unlabeled dat...
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04/08/2020
Empirical Perspectives on One-Shot Semi-supervised Learning
One of the greatest obstacles in the adoption of deep neural networks fo...
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10/23/2019
A Useful Taxonomy for Adversarial Robustness of Neural Networks
Adversarial attacks and defenses are currently active areas of research ...
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03/26/2018
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
Although deep learning has produced dazzling successes for applications ...
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08/23/2017
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates
In this paper, we show a phenomenon, which we named "super-convergence",...
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04/05/2017
Best Practices for Applying Deep Learning to Novel Applications
This report is targeted to groups who are subject matter experts in thei...
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02/14/2017
Exploring loss function topology with cyclical learning rates
We present observations and discussion of previously unreported phenomen...
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11/02/2016
Deep Convolutional Neural Network Design Patterns
Recent research in the deep learning field has produced a plethora of ne...
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11/22/2015
Gradual DropIn of Layers to Train Very Deep Neural Networks
We introduce the concept of dynamically growing a neural network during ...
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06/03/2015
Cyclical Learning Rates for Training Neural Networks
It is known that the learning rate is the most important hyper-parameter...
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05/06/2013
