9 citations · 23 across the 6 of their papers we have counts for
6 papers
When and how epochwise double descent happens
Cory Stephenson, Tyler Lee
Deep neural networks are known to exhibit a `double descent' behavior as the number of parameters increases. Recently, it has also been shown that an `epochwise double descent' eff…
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks
Landan Seguin, Anthony Ndirango, Neeli Mishra +2
Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Almost all defense strategies achieve this invariance through a…
Generalization in multitask deep neural classifiers: a statistical physics approach
Tyler Lee, Anthony Ndirango
A proper understanding of the striking generalization abilities of deep neural networks presents an enduring puzzle. Recently, there has been a growing body of numerically-grounded…
Label-efficient audio classification through multitask learning and self-supervision
Tyler Lee, Ting Gong, Suchismita Padhy +2
While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a…
Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems
Gokce Keskin, Tyler Lee, Cory Stephenson +1
This paper evaluates the effectiveness of a Cycle-GAN based voice converter (VC) on four speaker identification (SID) systems and an automated speech recognition (ASR) system for v…
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
Gokce Keskin, Tyler Lee, Cory Stephenson +1
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set. This property is enabled through speaker embed…