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Byung-Hak Kim

AKASA

15 papers hereh-index 9302 citations21 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author11
  • middle author2

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.IT5
  • cs.CV2
  • cs.CL1
  • cs.CY1
affiliations
  • AKASA
Homepage
same name
  • Byung-Hak Kim — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20102023
most citedGritNet: Student Performance Prediction with Deep Learning

91 citations · 164 across the 15 of their papers we have counts for

collaborators
Showing 2010Show all

4 papers · 1 filter

cs.IT2010★ 9 cited

An Iterative Joint Linear-Programming Decoding of LDPC Codes and Finite-State Channels

Byung-Hak Kim, Henry D. Pfister

In this paper, we introduce an efficient iterative solver for the joint linear-programming (LP) decoding of low-density parity-check (LDPC) codes and finite-state channels (FSCs).…

cs.IT2010★ 5 cited

IMP: A Message-Passing Algorithmfor Matrix Completion

Byung-Hak Kim, Arvind Yedla, Henry D. Pfister

A new message-passing (MP) method is considered for the matrix completion problem associated with recommender systems. We attack the problem using a (generative) factor graph model…

cs.IT2010★ 5 cited

On the Joint Decoding of LDPC Codes and Finite-State Channels via Linear Programming

Byung-Hak Kim, Henry D. Pfister

In this paper, the linear programming (LP) decoder for binary linear codes, introduced by Feldman, et al. is extended to joint-decoding of binary-input finite-state channels. In pa…

cs.IT2010★ 1 cited

Message-Passing Inference on a Factor Graph for Collaborative Filtering

Byung-Hak Kim, Arvind Yedla, Henry D. Pfister

This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.