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researcher

Ziwei Li

7 papers hereh-index 5174 citations19 works total

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

author position
  • first author3
  • middle author3

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CL1
  • cs.GR1
  • cs.IR1
same name
  • Ziwei Li — 12 papers, h 3
  • Ziwei Li — 7 papers, h 3
  • Ziwei Li — 5 papers, h 4
  • Ziwei Li — 4 papers, h 2
  • Ziwei Li — 3 papers, h 18
  • Ziwei Li — 2 papers, h 5

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
20212026
most citedLearn Like The Pro: Norms from Theory to Size Neural Computation

1 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles

Ziwei Li, Yuhan Duan, Tianyu Xiong +3

Surrogate models are essential for efficient exploration of large-scale ensemble simulations. Implicit neural representations (INRs) provide a compact and continuous framework for…

cs.LG2025★ 1 cited

Neural Approaches to SAT Solving: Design Choices and Interpretability

David Mojžíšek, Jan Hůla, Ziwei Li +2

In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mech…

cs.LG2024

FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction

Ziwei Li, Xiaoqi Wang, Hong-You Chen +2

Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. D…

cs.LG2021★ 1 cited

Learn Like The Pro: Norms from Theory to Size Neural Computation

Margaret Trautner, Ziwei Li, Sai Ravela

The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of…

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