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Found 6 papers, 3 papers with code
Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection
Secondly, we introduce a modality imputation network (MIN) pre-trained in a multimodal client for modality translation in unimodal clients and investigate its potential towards mitigating the missing modality problem.
The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection
Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning.
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment
This paper challenges this assumption and introduces FedPDA, a novel adaptation framework that brings the utility of learning from remote data from Federated Learning into PDA.
Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents
Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents.
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Furthermore, we combine image translation with a masked conditional diffusion model, which attempts to `imagine' what tissue exists under a masked area, further exposing unknown patterns as the generative model fails to recreate them.
Nonlinear pile-up separation with LSTM neural networks for cryogenic particle detectors
In high-background or calibration measurements with cryogenic particle detectors, a significant share of the exposure is lost due to pile-up of recoil events.
