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Martin Renqiang Min

4 papers here

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

author position
  • first author1
  • middle author1
  • last author2

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

fields
  • cs.CV2
  • cs.AI1
  • q-bio.QM1
ORCID 0000-0002-8563-6133
same name
  • Martin Renqiang Min — 15 papers, h 29
  • Martin Renqiang Min — 5 papers, h 5
  • Martin Renqiang Min — 4 papers, h 3
  • Martin Renqiang Min — 2 papers, h 4
  • Martin Renqiang Min — 2 papers, h 5
  • Martin Renqiang Min — 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

most citedConditional Image-to-Video Generation with Latent Flow Diffusion Models

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

collaborators

4 papers

cs.CV2023

Exploring Compositional Visual Generation with Latent Classifier Guidance

Changhao Shi, Haomiao Ni, Kai Li +3

Diffusion probabilistic models have achieved enormous success in the field of image generation and manipulation. In this paper, we explore a novel paradigm of using the diffusion m…

cs.CV2023★ 1 cited

Conditional Image-to-Video Generation with Latent Flow Diffusion Models

Haomiao Ni, Changhao Shi, Kai Li +2

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label li…

q-bio.QM2023

T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy

Ziqi Chen, Martin Renqiang Min, Hongyu Guo +3

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of…

cs.AI2016

A Shallow High-Order Parametric Approach to Data Visualization and Compression

Martin Renqiang Min, Hongyu Guo, Dongjin Song

Explicit high-order feature interactions efficiently capture essential structural knowledge about the data of interest and have been used for constructing generative models. We pre…

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