5 citations · 17 across the 8 of their papers we have counts for
8 papers
Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size
John Chen, Chen Dun, Anastasios Kyrillidis
Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels. However, recent performanc…
REX: Revisiting Budgeted Training with an Improved Schedule
John Chen, Cameron Wolfe, Anastasios Kyrillidis
Deep learning practitioners often operate on a computational and monetary budget. Thus, it is critical to design optimization algorithms that perform well under any budget. The lin…
Mitigating deep double descent by concatenating inputs
John Chen, Qihan Wang, Anastasios Kyrillidis
The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural network…
StackMix: A complementary Mix algorithm
John Chen, Samarth Sinha, Anastasios Kyrillidis
Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix:…
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge
Benjamin Coleman, Gaurav Gupta, John Chen +1
Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression…
Negative sampling in semi-supervised learning
John Chen, Vatsal Shah, Anastasios Kyrillidis
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of ne…