4 citations · 8 across the 11 of their papers we have counts for
5 papers · 1 filter
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis
Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network tra…
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe, Fangshuo Liao, Qihan Wang +2
Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-st…
Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
Junhyung Kim, Byungyoon Park, Charmgil Hong
We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label depend…
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim, JA Lara Benitez, Mohammad Taha Toghani +3
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.…
Fast quantum state reconstruction via accelerated non-convex programming
Junhyung Lyle Kim, George Kollias, Amir Kalev +2
We propose a new quantum state reconstruction method that combines ideas from compressed sensing, non-convex optimization, and acceleration methods. The algorithm, called Momentum-…