18 citations · 20 across the 4 of their papers we have counts for
6 papers · 1 filter
FROST: Faster and more Robust One-shot Semi-supervised Training
Helena E. Liu, Leslie N. Smith
Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is s…
Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance
Leslie N. Smith, Adam Conovaloff
Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for t…
Empirical Perspectives on One-Shot Semi-supervised Learning
Leslie N. Smith, Adam Conovaloff
One of the greatest obstacles in the adoption of deep neural networks for new applications is that training the network typically requires a large number of manually labeled traini…
A Useful Taxonomy for Adversarial Robustness of Neural Networks
Leslie N. Smith
Adversarial attacks and defenses are currently active areas of research for the deep learning community. A recent review paper divided the defense approaches into three categories;…
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
Leslie N. Smith
Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-param…
Exploring loss function topology with cyclical learning rates
Leslie N. Smith, Nicholay Topin
We present observations and discussion of previously unreported phenomena discovered while training residual networks. The goal of this work is to better understand the nature of n…