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

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

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…

cs.LG20247 cited

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

Ziyang Wu, Tianjiao Ding, Yifu Lu +6

The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However,…