72 citations · 542 across the 65 of their papers we have counts for
8 papers · 1 filter
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
Anastasios Kyrillidis, Anshumali Shrivastava, Moshe Y. Vardi +1
The Boolean SATisfiability problem (SAT) is of central importance in computer science. Although SAT is known to be NP-complete, progress on the engineering side, especially that of…
Optimal Mini-Batch Size Selection for Fast Gradient Descent
Michael P. Perrone, Haidar Khan, Changhoan Kim +3
This paper presents a methodology for selecting the mini-batch size that minimizes Stochastic Gradient Descent (SGD) learning time for single and multiple learner problems. By deco…
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…
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…
Demon: Improved Neural Network Training with Momentum Decay
John Chen, Cameron Wolfe, Zhao Li +1
Momentum is a widely used technique for gradient-based optimizers in deep learning. In this paper, we propose a decaying momentum (\textsc{Demon}) rule. We conduct the first large-…
Distributed Learning of Deep Neural Networks using Independent Subnet Training
Binhang Yuan, Cameron R. Wolfe, Chen Dun +3
Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, thus enabling reductions in training time. Distributed learning parti…