Leslie N. Smith

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All published works

Action	Title	Year	Authors
+ PDF Chat	Symmetry constrained neural networks for detection and localization of damage in metal plates	2024	James Amarel Christopher Rudolf Athanasios Iliopoulos John G. Michopoulos Leslie N. Smith
+ PDF Chat	Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance	2022	Leslie N. Smith Adam Conovaloff
+	General Cyclical Training of Neural Networks	2022	Leslie N. Smith
+	Cyclical Focal Loss	2022	Leslie N. Smith
+ PDF Chat	An Approach to Partial Observability in Games: Learning to Both Act and Observe	2021	Elizabeth Gilmour Noah Plotkin Leslie N. Smith
+	Empirical Perspectives on One-Shot Semi-supervised Learning	2020	Leslie N. Smith Adam Conovaloff
+	FROST: Faster and more Robust One-shot Semi-supervised Training	2020	Helena E. Liu Leslie N. Smith
+	Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance	2020	Leslie N. Smith A. Conovaloff
+	Empirical Perspectives on One-Shot Semi-supervised Learning	2020	Leslie N. Smith Adam Conovaloff
+	A Useful Taxonomy for Adversarial Robustness of Neural Networks	2019	Leslie N. Smith
+ PDF Chat	Super-convergence: very fast training of neural networks using large learning rates	2019	Leslie N. Smith Nicholay Topin
+	A Useful Taxonomy for Adversarial Robustness of Neural Networks	2019	Leslie N. Smith
+	A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay	2018	Leslie N. Smith
+	Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates	2017	Leslie N. Smith Nicholay Topin
+	Best Practices for Applying Deep Learning to Novel Applications.	2017	Leslie N. Smith
+ PDF Chat	Cyclical Learning Rates for Training Neural Networks	2017	Leslie N. Smith
+	Exploring loss function topology with cyclical learning rates	2017	Leslie N. Smith Nicholay Topin
+	Best Practices for Applying Deep Learning to Novel Applications	2017	Leslie N. Smith
+	Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates	2017	Leslie N. Smith Nicholay Topin
+ PDF Chat	Gradual DropIn of Layers to Train Very Deep Neural Networks	2016	Leslie N. Smith Emily M. Hand Timothy Doster
+	Deep Convolutional Neural Network Design Patterns	2016	Leslie N. Smith Nicholay Topin
+	Gradual DropIn of Layers to Train Very Deep Neural Networks	2015	Leslie N. Smith Emily M. Hand Timothy Doster
+	No More Pesky Learning Rate Guessing Games.	2015	Leslie N. Smith
+	Cyclical Learning Rates for Training Neural Networks	2015	Leslie N. Smith
+	Convolutional Architecture Exploration for Action Recognition and Image Classification	2015	Jack Turner David W. Aha Leslie N. Smith Kalyan Moy Gupta
+	Cyclical Learning Rates for Training Neural Networks	2015	Leslie N. Smith
+	Gradual DropIn of Layers to Train Very Deep Neural Networks	2015	Leslie N. Smith Emily M. Hand Timothy Doster
+ PDF Chat	How to find real-world applications of compressive sensing	2013	Leslie N. Smith

Common Coauthors

Coauthor	Papers Together
Nicholay Topin	5
Adam Conovaloff	3
Timothy Doster	3
Emily M. Hand	3
Jack Turner	1
Helena E. Liu	1
David W. Aha	1
Noah Plotkin	1
John G. Michopoulos	1
Elizabeth Gilmour	1
Christopher Rudolf	1
James Amarel	1
Kalyan Moy Gupta	1
A. Conovaloff	1
Athanasios Iliopoulos	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	6
+	Very Deep Convolutional Networks for Large-Scale Image Recognition	2014	Karen Simonyan Andrew Zisserman	5
+ PDF Chat	Cyclical Learning Rates for Training Neural Networks	2017	Leslie N. Smith	5
+	ADADELTA: An Adaptive Learning Rate Method	2012	Matthew D. Zeiler	4
+ PDF Chat	Show and tell: A neural image caption generator	2015	Oriol Vinyals Alexander Toshev Samy Bengio Dumitru Erhan	4
+ PDF Chat	Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation	2014	Ross Girshick Jeff Donahue Trevor Darrell Jitendra Malik	4
+	ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient	2014	Çaǧlar Gülçehre Yoshua Bengio	4
+	No More Pesky Learning Rate Guessing Games.	2015	Leslie N. Smith	4
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	4
+	An Empirical Evaluation of Deep Learning on Highway Driving	2015	Brody Huval Tao Wang Sameep Tandon Jeff Kiske Will Song Joel Pazhayampallil Mykhaylo Andriluka Pranav Rajpurkar Toki Migimatsu Royce Cheng-Yue	4
+	Sequence to Sequence Learning with Neural Networks	2014	Ilya Sutskever Oriol Vinyals Quoc V. Le	3
+	No More Pesky Learning Rates	2012	Tom Schaul Sixin Zhang Yann LeCun	3
+	Improving neural networks by preventing co-adaptation of feature detectors	2012	Geoffrey E. Hinton Nitish Srivastava Alex Krizhevsky Ilya Sutskever Ruslan Salakhutdinov	3
+	Qualitatively characterizing neural network optimization problems	2014	Ian Goodfellow Oriol Vinyals Andrew Saxe	3
+	Wide Residual Networks	2016	Sergey Zagoruyko Nikos Komodakis	3
+	Don't Decay the Learning Rate, Increase the Batch Size	2017	Samuel Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le	3
+	Three Factors Influencing Minima in SGD	2017	Stanisław Jastrzȩbski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey	3
+	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	2020	Kihyuk Sohn David Berthelot Chun‐Liang Li Zizhao Zhang Nicholas Carlini Ekin D. Cubuk А.В. Куракин Han Zhang Colin Raffel	3
+	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	2019	David Berthelot Nicholas Carlini Ekin D. Cubuk А.В. Куракин Kihyuk Sohn Han Zhang Colin Raffel	3
+ PDF Chat	Going deeper with convolutions	2015	Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke Andrew Rabinovich	3
+	RandAugment: Practical automated data augmentation with a reduced search space	2019	Ekin D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le	3
+	RandAugment: Practical data augmentation with no separate search.	2019	Ekin D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le	3
+	SGDR: Stochastic Gradient Descent with Warm Restarts	2016	Ilya Loshchilov Frank Hutter	3
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	3
+	Self-training with Noisy Student improves ImageNet classification	2019	Qizhe Xie Minh-Thang Luong Eduard Hovy Quoc V. Le	3
+	Densely Connected Convolutional Networks	2016	Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger	3
+	Deep Networks with Stochastic Depth	2016	Gao Huang Yu Sun Zhuang Liu Daniel Sedra Kilian Q. Weinberger	3
+	Unsupervised Data Augmentation for Consistency Training	2019	Qizhe Xie Zihang Dai Eduard Hovy Minh-Thang Luong Quoc V. Le	2
+	Matching networks for one shot learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra	2
+	No More Pesky Learning Rates	2012	Tom Schaul Sixin Zhang Yann LeCun	2
+ PDF Chat	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Hot Swapping for Online Adaptation of Optimization Hyperparameters	2014	Kevin Bache Dennis DeCoste Padhraic Smyth	2
+	The Effects of Hyperparameters on SGD Training of Neural Networks	2015	Thomas M. Breuel	2
+	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	Going Deeper with Convolutions	2014	Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke Andrew Rabinovich	2
+ PDF Chat	Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning	2018	Takeru Miyato Shin‐ichi Maeda Masanori Koyama Shin Ishii	2
+	Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	2019	Antreas Antoniou Amos Storkey	2
+	A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay	2018	Leslie N. Smith	2
+	Interpolation Consistency Training for Semi-Supervised Learning	2019	Vikas Verma Kenji Kawaguchi Alex Lamb Juho Kannala Yoshua Bengio David López-Paz	2
+	Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes	2017	Lei Wu Zhanxing Zhu E Weinan	2
+	Possible Mechanisms for Neural Reconfigurability and their Implications	2015	Thomas M. Breuel	2
+	Improved Regularization of Convolutional Neural Networks with Cutout	2017	Terrance DeVries Graham W. Taylor	2
+	Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour	2017	Priya Goyal Piotr Dollár Ross Girshick Pieter Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He	2
+	Noisy Networks for Exploration	2017	Meire Fortunato Mohammad Gheshlaghi Azar Bilal Piot Jacob Menick Ian Osband Alex Graves Vlad Mnih Rémi Munos Demis Hassabis Olivier Pietquin	2
+	S4L: Self-Supervised Semi-Supervised Learning	2019	Xiaohua Zhai Avital Oliver Alexander Kolesnikov Lucas Beyer	2
+	Unsupervised Learning via Meta-Learning	2018	Kyle Hsu Sergey Levine Chelsea Finn	2
+	Random Walk Initialization for Training Very Deep Feedforward Networks	2014	David Sussillo L. F. Abbott	2
+	Prototypical Networks for Few-shot Learning	2017	Jake Snell Kevin Swersky Richard S. Zemel	2
+	Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning	2016	Qianli Liao Kenji Kawaguchi Tomaso Poggio	2
+	An empirical analysis of the optimization of deep network loss surfaces	2016	Daniel Jiwoong Im Michael Tao Kristin Branson	2