Leslie N. Smith | DeepAI

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Nicholay Topin
18 publications
Timothy Doster
5 publications
Emily M. Hand
3 publications
Adam Conovaloff
2 publications
Helena E. Liu
1 publication

∙ 02/17/2022

General Cyclical Training of Neural Networks

This paper describes the principle of "General Cyclical Training" in mac...

0 Leslie N. Smith, et al. ∙

∙ 02/16/2022

Cyclical Focal Loss

The cross-entropy softmax loss is the primary loss function used to trai...

0 Leslie N. Smith, et al. ∙

∙ 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...

0 Helena E. Liu, et al. ∙

∙ 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...

0 Leslie N. Smith, et al. ∙

∙ 04/08/2020

Empirical Perspectives on One-Shot Semi-supervised Learning

One of the greatest obstacles in the adoption of deep neural networks fo...

0 Leslie N. Smith, et al. ∙

∙ 10/23/2019

A Useful Taxonomy for Adversarial Robustness of Neural Networks

Adversarial attacks and defenses are currently active areas of research ...

0 Leslie N. Smith, et al. ∙

∙ 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 ...

0 Leslie N. Smith, et al. ∙

∙ 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",...

0 Leslie N. Smith, et al. ∙

∙ 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...

0 Leslie N. Smith, et al. ∙

∙ 02/14/2017

Exploring loss function topology with cyclical learning rates

We present observations and discussion of previously unreported phenomen...

0 Leslie N. Smith, et al. ∙

∙ 11/02/2016

Deep Convolutional Neural Network Design Patterns

Recent research in the deep learning field has produced a plethora of ne...

0 Leslie N. Smith, et al. ∙

∙ 11/22/2015

Gradual DropIn of Layers to Train Very Deep Neural Networks

We introduce the concept of dynamically growing a neural network during ...

0 Leslie N. Smith, et al. ∙

∙ 06/03/2015

Cyclical Learning Rates for Training Neural Networks

It is known that the learning rate is the most important hyper-parameter...

0 Leslie N. Smith, et al. ∙

∙ 05/06/2013

How to find real-world applications for compressive sensing

The potential of compressive sensing (CS) has spurred great interest in ...

0 Leslie N. Smith, et al. ∙

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