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Yanping Huang

26 papers hereh-index 2119.3k citations27 works total

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

author position
  • sole author1
  • first author2
  • middle author18
  • last author1

Across the 22 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG7
  • cs.CV4
  • cs.AI1
  • cs.DC1
  • cs.NE1
same name
  • Yanping Huang — 4 papers, h 5
  • Yanping Huang — 4 papers
  • Yanping Huang — 2 papers, h 4
  • Yanping Huang — 2 papers, h 2
  • Yanping Huang — 2 papers, h 0
  • Yanping Huang — 2 papers

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
20152024
most citedScaling Instruction-Finetuned Language Models

1.2k citations · 3.5k across the 25 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 2 cited

Stylus: Automatic Adapter Selection for Diffusion Models

Michael Luo, Justin Wong, Brandon Trabucco +5

Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters…

cs.CV2023★ 7 cited

SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs

Lijun Yu, Yong Cheng, Zhiruo Wang +10

In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such…

cs.CV2020★ 1 cited

Do CNNs Encode Data Augmentations?

Eddie Yan, Yanping Huang

Data augmentations are important ingredients in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network feat…

cs.CV2018★ 256 cited

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Yanping Huang, Youlong Cheng, Ankur Bapna +8

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing m…

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