Hugging Face Trending Papers June 3, 2026

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

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To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.

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arXiv Machine Learning

Jun 4

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.

By Vasiliki Rizou, Pascal Frossard, Dorina Thanou

rag multimodal benchmarks safety

arXiv Machine Learning

Aug 12

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

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

diffusion multimodal

arXiv Machine Learning

Jun 30

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks

arXiv:2503. 09679v2 Announce Type: replace Abstract: Meta-learning represents a strong class of approaches for solving few-shot learning tasks.

By Wei Cui, Tongzi Wu, Jesse C. Cresswell, Yi Sui, Keyvan Golestan

arXiv Machine Learning

Jul 21

MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.

By Sana Tonekaboni, Viktoria Schuster, Caroline Uhler

fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Sep 1

Diverse via bounded Agreement: Geometric Regularization for Multimodal Fusion

arXiv:2601.21670v4 Announce Type: replace-cross Abstract: Multimodal fusion is often treated as an optimization-balancing problem, where training signals are adjusted to prevent one modality from dom...

By Zixuan Xia, Hao Wang, Pengcheng Weng, Yanyu Qian, Yangxin Xu, William Dan, Fei Wang

rag multimodal benchmarks safety

Hugging Face Trending Papers

Jul 21

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.

multimodal

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Related stories

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks

MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

Diverse via bounded Agreement: Geometric Regularization for Multimodal Fusion

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention