activity
20182021
most citedShallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution

1 citations · 1 across the 2 of their papers we have counts for

collaborators

7 papers

math.ST2021

Detection of Signal in the Spiked Rectangular Models

Ji Hyung Jung, Hye Won Chung, Ji Oon Lee

We consider the problem of detecting signals in the rank-one signal-plus-noise data matrix models that generalize the spiked Wishart matrices. We show that the principal component…

cs.LG2021

Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Jinhee Lee, Haeri Kim, Youngkyu Hong +1

Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data manifol…

stat.ML2020

Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

Jeonghwan Lee, Daesung Kim, Hye Won Chung

We study hypergraph clustering in the weighted -uniform hypergraph stochastic block model (\textsf{-WHSBM}), where each edge consisting of nodes from the same community h…

math.ST2020

Weak Detection in the Spiked Wigner Model with General Rank

Ji Hyung Jung, Hye Won Chung, Ji Oon Lee

We study the statistical decision process of detecting the signal from a `signal+noise' type matrix model with an additive Wigner noise. We propose a hypothesis test based on the l…

cs.LG20191 cited

Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution

Youngjae Min, Hye Won Chung

Guiding the design of neural networks is of great importance to save enormous resources consumed on empirical decisions of architectural parameters. This paper constructs shallow s…

cs.IT2018

Parity Queries for Binary Classification

Hye Won Chung, Ji Oon Lee, Doyeon Kim +1

Consider a query-based data acquisition problem that aims to recover the values of binary variables from parity (XOR) measurements of chosen subsets of the variables. Assume th…