arXiv:2607. 27660v1 Announce Type: new Abstract: Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and multimodal learning.
By Rishabh Iyer, Truong Pham, Anay Majee
arXiv:2609.23533v1 Announce Type: new
Abstract: Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of ot...
By Zechang Xiong, Da Li, Rong Yin, Kexin Tang, Biao Yang, Pengyuan Li, Wenkang Kong, Yulan Hu, Shengyu Zhu, Hao Peng
Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise supervision costs, but separate hub connections can...
arXiv:2608. 10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning.
By Viktoria Schuster, Sana Tonekaboni, Caroline Uhler
arXiv:2609.17094v1 Announce Type: new
Abstract: Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise su...
By Ying Guo, Haidong Chen, Linrui Xu, Xiaohao Liu, Chuancheng Shi, Canran Xiao, Dan Zhang, Fei Shen, Li Shen, Tat-Seng Chua
arXiv:2606. 04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood.
By Justinas Zaliaduonis, Patrick Putzky, Till Richter, Sergios Gatidis