Cover Image

Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hemker, Konstantin, Simidjievski, Nikola, Jamnik, Mateja
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
by: Hemker, Konstantin, et al.
Published: (2023)

Measuring Cross-Modal Interactions in Multimodal Models
by: Wenderoth, Laura, et al.
Published: (2024)

PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis
by: Buzzard, Zak, et al.
Published: (2024)

TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
by: Margeloiu, Andrei, et al.
Published: (2024)

LLM Embeddings for Deep Learning on Tabular Data
by: Koloski, Boshko, et al.
Published: (2025)

RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
by: Matjašec, Urška, et al.
Published: (2025)

How Well Does Your Tabular Generator Learn the Structure of Tabular Data?
by: Jiang, Xiangjian, et al.
Published: (2025)

TabStruct: Measuring Structural Fidelity of Tabular Data
by: Jiang, Xiangjian, et al.
Published: (2025)

TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
by: Margeloiu, Andrei, et al.
Published: (2024)

ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data
by: Jiang, Xiangjian, et al.
Published: (2023)

GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
by: Margeloiu, Andrei, et al.
Published: (2022)

Tabular Foundation Model for Generative Modelling
by: Jiang, Xiangjian, et al.
Published: (2026)

Hierarchical Concept-based Interpretable Models
by: Hill, Oscar, et al.
Published: (2026)

Towards Spatial Transcriptomics-driven Pathology Foundation Models
by: Hemker, Konstantin, et al.
Published: (2026)

Digging Deeper: Learning Multi-Level Concept Hierarchies
by: Hill, Oscar, et al.
Published: (2026)

Do Concept Bottleneck Models Respect Localities?
by: Raman, Naveen, et al.
Published: (2024)

Learning to Receive Help: Intervention-Aware Concept Embedding Models
by: Zarlenga, Mateo Espinosa, et al.
Published: (2023)

Foundations of Interpretable Models
by: Barbiero, Pietro, et al.
Published: (2025)

Foundation Model in Biomedicine
by: Liu, Xiangrui, et al.
Published: (2025)

MAny: Merge Anything for Multimodal Continual Instruction Tuning
by: Gao, Zijian, et al.
Published: (2026)

Efficient Bias Mitigation Without Privileged Information
by: Zarlenga, Mateo Espinosa, et al.
Published: (2024)

UPath: Universal Planner Across Topological Heterogeneity For Grid-Based Pathfinding
by: Ananikian, Aleksandr, et al.
Published: (2026)

Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
by: Zhang, Dingkun, et al.
Published: (2025)

MergeIT: From Selection to Merging for Efficient Instruction Tuning
by: Cai, Hongyi, et al.
Published: (2025)

Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging
by: Su, Guinan, et al.
Published: (2025)

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
by: Zarlenga, Mateo Espinosa, et al.
Published: (2025)

MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities
by: Li, Kunxi, et al.
Published: (2024)

Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
by: Ju, Yiming, et al.
Published: (2024)

ATM: Improving Model Merging by Alternating Tuning and Merging
by: Zhou, Luca, et al.
Published: (2024)

MIN-Merging: Merge the Important Neurons for Model Merging
by: Liang, Yunfei
Published: (2025)

Actionable Interpretability Must Be Defined in Terms of Symmetries
by: Barbiero, Pietro, et al.
Published: (2026)

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
by: Andreychuk, Anton, et al.
Published: (2025)

MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
by: Chen, Tao, et al.
Published: (2023)

Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting
by: Tran, Trong Khiem, et al.
Published: (2026)

Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
by: Balne, Charith Chandra Sai, et al.
Published: (2024)

H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
by: Guo, Wei, et al.
Published: (2025)

Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)

Reward Sharpness-Aware Fine-Tuning for Diffusion Models
by: Kim, Kwanyoung, et al.
Published: (2026)

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)

Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models
by: Chijiwa, Daiki, et al.
Published: (2025)