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Eunhyeok Park

13 papers hereh-index 162.6k citations32 works total

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

author position
  • first author3
  • middle author6
  • last author4

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.LG3
  • cs.IR1
  • cs.NE1
same name
  • Eunhyeok Park — 12 papers, h 5
  • Eunhyeok Park — 10 papers, h 4
  • Eunhyeok Park — 2 papers, h 2
  • Eunhyeok Park — 1 paper, h 1

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
20152023
most citedCompression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

115 citations · 205 across the 11 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022

Symmetry Regularization and Saturating Nonlinearity for Robust Quantization

Sein Park, Yeongsang Jang, Eunhyeok Park

Robust quantization improves the tolerance of networks for various implementations, allowing reliable output in different bit-widths or fragmented low-precision arithmetic. In this…

cs.LG2022

NIPQ: Noise proxy-based Integrated Pseudo-Quantization

Juncheol Shin, Junhyuk So, Sein Park +3

Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware…

cs.CV2022

Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking

Ilchae Jung, Minji Kim, Eunhyeok Park +1

This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural netwo…

cs.CV2022

INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold

Changhun Lee, Hyungjun Kim, Eunhyeok Park +1

Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degrada…

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