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Yunming Ye

17 papers hereh-index 4710.8k citations240 works total

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

author position
  • middle author11
  • last author4

Across the 15 of 17 papers where every author was matched, so the position is known.

fields
  • cs.IR7
  • cs.CV6
  • cs.LG4
same name
  • Yunming Ye — 20 papers, h 10
  • Yunming Ye — 1 paper, h 0

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
20162023
most citedDeepFM: A Factorization-Machine based Neural Network for CTR Prediction

542 citations · 744 across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 54 cited

SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification

Baoquan Zhang, Shanshan Feng, Xutao Li +2

Few-Shot Remote Sensing Scene Classification (FSRSSC) is an important task, which aims to recognize novel scene classes with few examples. Recently, several studies attempt to addr…

cs.LG2021★ 2 cited

RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting

Chuyao Luo, ZhengZhang, Rui Ye +2

Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasin…

cs.CV2021

Prototype Completion for Few-Shot Learning

Baoquan Zhang, Xutao Li, Yunming Ye +1

Few-shot learning aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tun…

cs.CV2021★ 3 cited

MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning

Baoquan Zhang, Xutao Li, Shanshan Feng +2

Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize novel classes with few examples? Pre-training based methods effectively tackle the problem by pre-train…

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