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Zenan Ling

9 papers hereh-index 336 citations9 works total

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

author position
  • first author1
  • middle author5
  • last author2

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV1
  • stat.ML1
same name
  • Zenan Ling — 18 papers, h 13
  • Zenan Ling — 8 papers, h 2
  • Zenan Ling — 1 paper
  • Zenan Ling — 1 paper, h 2

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
20232026
most citedRevisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification

2 citations · 2 across the 8 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024

Series-to-Series Diffusion Bridge Model

Hao Yang, Zhanbo Feng, Feng Zhou +2

Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in determ…

cs.LG2024

IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks

Junchao Lin, Zenan Ling, Zhanbo Feng +6

Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range depende…

stat.ML2024

Nonstationary Sparse Spectral Permanental Process

Zicheng Sun, Yixuan Zhang, Zenan Ling +2

Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel appr…

cs.LG2024

Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures

Zenan Ling, Longbo Li, Zhanbo Feng +4

Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding…

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