86 citations · 259 across the 31 of their papers we have counts for
4 papers · 1 filter
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
Tianyu Yu, Zefan Wang, Chongyi Wang +31
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged a…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
Yuan Yao, Xutao Li, Yu Zhang +1
Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing H…
Heterogeneous Domain Adaptation via Soft Transfer Network
Yuan Yao, Yu Zhang, Xutao Li +1
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a…