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Zhiwei Fang

4 papers hereh-index 106.3k citations20 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.IR2
  • cs.CV1
  • cs.LG1
same name
  • Zhiwei Fang — 3 papers, h 2
  • Zhiwei Fang — 2 papers, h 3
  • Zhiwei Fang — 2 papers, h 3
  • Zhiwei Fang — 1 paper
  • Zhiwei Fang — 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
20182023
most citedDual Attention Network for Scene Segmentation

212 citations · 219 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2023★ 7 cited

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach

Zhiwei Fang, Sifan Wang, Paris Perdikaris

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbatio…

cs.IR2022

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction

Xiaoxiao Xu, Zhiwei Fang, Qian Yu +7

The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling brin…

cs.IR2022

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

Xiaoxiao Xu, Chen Yang, Qian Yu +7

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alle…

cs.CV2018★ 212 cited

Dual Attention Network for Scene Segmentation

Jun Fu, Jing Liu, Haijie Tian +4

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by…

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