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Yihua Cheng

17 papers hereh-index 131.3k citations33 works total

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

author position
  • first author6
  • middle author10

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.NI4
  • cs.CL2
  • cs.LG2
  • cs.MM2
  • cs.OS2
same name
  • Yihua Cheng — 12 papers, h 7
  • Yihua Cheng — 5 papers, h 2
  • Yihua Cheng — 3 papers, h 6
  • Yihua Cheng — 1 paper
  • Yihua Cheng — 1 paper, h 3

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
20212025
most citedAppearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

67 citations · 103 across the 17 of their papers we have counts for

collaborators
Showing cs.NIShow all

4 papers · 1 filter

cs.NI2025

Towards More Economical Context-Augmented LLM Generation by Reusing Stored KV Cache

Hanchen Li, Yuhan Liu, Yihua Cheng +2

Across large language model (LLM) applications, we observe an emerging trend for reusing KV caches to save the prefill delays of processing repeated input texts in different LLM in…

cs.NI2024

Earth+: on-board satellite imagery compression leveraging historical earth observations

Kuntai Du, Yihua Cheng, Peder Olsen +3

With the increasing deployment of earth observation satellite constellations, the downlink (satellite-to-ground) capacity often limits the freshness, quality, and coverage of the i…

cs.NI2024★ 8 cited

Eloquent: A More Robust Transmission Scheme for LLM Token Streaming

Hanchen Li, Yuhan Liu, Yihua Cheng +3

To render each generated token in real-time for users, the Large Language Model (LLM) server generates tokens one by one and streams each token (or group of a few tokens) through t…

cs.NI2023

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

Yuhan Liu, Hanchen Li, Yihua Cheng +11

As large language models (LLMs) take on complex tasks, their inputs are supplemented with longer contexts that incorporate domain knowledge. Yet using long contexts is challenging,…

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