DMIL is a multimodal learning framework that decomposes sample-specific interactions into redundant, unique, and synergistic components via variational architecture and uses them for adaptive fine-tuning.
Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably),
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
FedMChain improves multimodal federated learning by chaining modality-wise optimization phases with error-compensated regularization and sparse sign-guided aggregation to mitigate modality competition and cut communication overhead.
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.
citing papers explorer
-
Information-Theoretic Decomposition for Multimodal Interaction Learning
DMIL is a multimodal learning framework that decomposes sample-specific interactions into redundant, unique, and synergistic components via variational architecture and uses them for adaptive fine-tuning.
-
Boosting Multimodal Federated Learning via Chained Modality Optimization
FedMChain improves multimodal federated learning by chaining modality-wise optimization phases with error-compensated regularization and sparse sign-guided aggregation to mitigate modality competition and cut communication overhead.
-
Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
PITA, a new semi-supervised deep learning algorithm, outperforms prior photo-z methods by using a triple-task loss on images, colors, and available redshifts to produce a smooth latent space.