arXiv AI
Jul 7
Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST
arXiv:2607. 04241v1 Announce Type: cross Abstract: Plasma disruption is a critical threat to tokamak safety.
FusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion
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arXiv AI
Jun 8
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
arXiv:2602. 15084v2 Announce Type: replace-cross Abstract: We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous diagnostics from the publicly available MAST dataset.
arXiv Machine Learning
Jun 3
Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals
arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.
arXiv AI
Jun 3
CL-DMDF:Dynamic Multimodal Data Fusion Model Based on Contrastive Learning
arXiv:2606. 02659v1 Announce Type: cross Abstract: Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making.
arXiv AI
Jun 12
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
arXiv:2602. 10132v3 Announce Type: replace-cross Abstract: Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors readings.
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.
arXiv Computation and Language
3d ago
Fusion Anything: A Generalized Multimodal Foundation Model
The paper introduces Fusion Anything Model (FAM), a foundation model designed for generalized multimodal data fusion that can handle arbitrary modality combinations and prediction tasks. FAM is trained on large-scale synthetic multimodal datasets generated via Structural Multimodal Causal Models (SMCMs), enabling it to encode transferable multimodal correlations. Experiments on 18 real-world datasets across 12 modalities and 11 tasks show that FAM performs competitively with specialized models without requiring task-specific adaptation.
arXiv Machine Learning
Jul 24
Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion
arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.
arXiv Machine Learning
Sep 22
Generalized Multimodal Foundation Model
The paper introduces a generalized multimodal foundation model that can handle arbitrary combinations of modalities and prediction tasks. It trains on large-scale synthetic multimodal datasets with diverse causal structures to learn transferable multimodal correlations. Experiments on 18 real-world datasets across 12 modalities and 11 tasks show competitive performance compared to specialized models without task-specific adaptation.
arXiv Machine Learning
Jul 7
Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
arXiv:2607. 05019v1 Announce Type: new Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks.
arXiv AI
Jun 17
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.
arXiv Machine Learning
Aug 5
DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.