Leslie N. Smith

23 papers A* 1A 1C 1Journal 20

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation. CoRR Sarah E. Wessinger, Leslie N. Smith, Jacob Gull, Jonathan Gehman, Zachary Beever, Andrew J. Kammerer
2024	J	`jnl`	Symmetry constrained neural networks for detection and localization of damage in metal plates. CoRR James Amarel, Christopher Rudolf, Athanasios Iliopoulos, John G. Michopoulos, Leslie N. Smith
2023	J	`jnl`	Editorial: Deep learning with limited labeled data for vision, audio, and text. Frontiers Artif. Intell. Marko Orescanin, Leslie N. Smith, Saurabh Sahu, Palash Goyal, Sujit Rokka Chhetri
2023	J	`jnl`	General Cyclical Training of Neural Networks. Adv. Artif. Intell. Mach. Learn. Leslie N. Smith
2022	J	`jnl`	Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance. Frontiers Artif. Intell. Leslie N. Smith, Adam Conovaloff
2022	J	`jnl`	Cyclical Focal Loss. CoRR Leslie N. Smith
2022	J	`jnl`	General Cyclical Training of Neural Networks. CoRR Leslie N. Smith
2021	C	`conf`	An Approach to Partial Observability in Games: Learning to Both Act and Observe. CoG Elizabeth Gilmour, Noah Plotkin, Leslie N. Smith
2021	J	`jnl`	An Approach to Partial Observability in Games: Learning to Both Act and Observe. CoRR Elizabeth Gilmour, Noah Plotkin, Leslie N. Smith
2020	J	`jnl`	Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance. CoRR Leslie N. Smith, Adam Conovaloff
2020	J	`jnl`	Empirical Perspectives on One-Shot Semi-supervised Learning. CoRR Leslie N. Smith, Adam Conovaloff
2019	J	`jnl`	A Useful Taxonomy for Adversarial Robustness of Neural Networks. CoRR Leslie N. Smith
2018	J	`jnl`	A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay. CoRR Leslie N. Smith
2017	J	`jnl`	Best Practices for Applying Deep Learning to Novel Applications. CoRR Leslie N. Smith
2017	A	`conf`	Cyclical Learning Rates for Training Neural Networks. WACV Leslie N. Smith
2017	J	`jnl`	Exploring loss function topology with cyclical learning rates. CoRR Leslie N. Smith, Nicholay Topin
2017	J	`jnl`	Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. CoRR Leslie N. Smith, Nicholay Topin
2016	J	`jnl`	Deep Convolutional Neural Network Design Patterns. CoRR Leslie N. Smith, Nicholay Topin
2016	A*	`conf`	Gradual DropIn of Layers to Train Very Deep Neural Networks. CVPR Leslie N. Smith, Emily M. Hand, Timothy Doster
2015	J	`jnl`	Gradual DropIn of Layers to Train Very Deep Neural Networks. CoRR Leslie N. Smith, Emily M. Hand, Timothy Doster
2015	J	`jnl`	No More Pesky Learning Rate Guessing Games. CoRR Leslie N. Smith
2013	J	`jnl`	How to find real-world applications for compressive sensing CoRR Leslie N. Smith
2013	J	`jnl`	Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse. IEEE Signal Process. Lett. Leslie N. Smith, Michael Elad