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researcher

Yi Ma

8 papers hereh-index 388 citations9 works total

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

author position
  • middle author4
  • last author4

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

fields
  • cs.CL3
  • cs.CV3
  • cs.LG1
  • cs.SD1
same name
  • Yi Ma — 39 papers, h 8
  • Yi Ma — 27 papers, h 12
  • Yi Ma — 26 papers, h 24
  • Yi Ma — 21 papers, h 73
  • Yi Ma — 18 papers, h 10
  • Yi Ma — 10 papers, h 7

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
20242026
most citedToken Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

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

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data

Yunxin Li, Xinyu Chen, Shenyuan Jiang +9

We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…

cs.SD2025

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Hardik B. Sailor, Aw Ai Ti, Chen Fang Yih Nancy +26

We present MERaLiON-SER, a robust speech emotion recognition model designed for English and Southeast Asian languages. The model is trained using a hybrid objective combining weigh…

cs.CV2025

Language-Image Alignment with Fixed Text Encoders

Jingfeng Yang, Ziyang Wu, Yue Zhao +1

Currently, the most dominant approach to establishing language-image alignment is to pre-train text and image encoders jointly through contrastive learning, such as CLIP and its va…

cs.CV2025

Simplifying DINO via Coding Rate Regularization

Ziyang Wu, Jingyuan Zhang, Druv Pai +5

DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned representations often enable state-of-t…

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