241 citations · 544 across the 9 of their papers we have counts for
9 papers
Analog CMOS-based Resistive Processing Unit for Deep Neural Network Training
Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee +1
Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training…
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
Calvin McCarter, Seyoung Kim
This paper addresses the problem of scalable optimization for L1-regularized conditional Gaussian graphical models. Conditional Gaussian graphical models generalize the well-known…
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Seyoung Kim, Padhraic Smyth, Stefan Luther
In this paper we describe a general probabilistic framework for modeling waveforms such as heartbeats from ECG data. The model is based on segmental hidden Markov models (as used i…
Feature Selection via Block-Regularized Regression
Seyoung Kim, Eric P. Xing
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one…
Smoothing Proximal Gradient Method for General Structured Sparse Learning
Xi Chen, Qihang Lin, Seyoung Kim +2
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input…
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso
Xi Chen, Seyoung Kim, Qihang Lin +2
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response vari…