Results 21 to 30 of about 8,339 (216)
Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models
Frontiers in Computer Science, 2021 We propose a method for multimodal concept formation. In this method, unsupervised multimodal clustering and cross-modal inference, as well as unsupervised representation learning, can be performed by integrating the multimodal latent Dirichlet ...
Ryo Kuniyasu +4 more
doaj +1 more source
Calibrating Multimodal Learning
, 2023 Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial ...
Zhang, Huan Ma. Qingyang +5 more
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Deep Multimodal Representation Learning: A Survey
IEEE Access, 2019 Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data.
Wenzhong Guo, Jianwen Wang, Shiping Wang
doaj +1 more source
Scientific Reports, 2023 The positron emission tomography (PET) with 18F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively unimpaired (CU) individuals.
Sang Won Park +4 more
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Digital twins to accelerate target identification and drug development for immune‐mediated disorders
FEBS Open Bio, EarlyView.Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley +1 more source
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning
IEEE Access, 2023 Multimodal transfer learning aims to transform pretrained representations of diverse modalities into a common domain space for effective multimodal fusion. However, conventional systems are typically built on the assumption that all modalities exist, and
Yanan Wang +3 more
doaj +1 more source
, 2023 This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in
Akkus, Cem +16 more
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Multimodal Federated Learning: A Survey
Sensors, 2023 Federated learning (FL), which provides a collaborative training scheme for distributed data sources with privacy concerns, has become a burgeoning and attractive research area. Most existing FL studies focus on taking unimodal data, such as image and text, as the model input and resolving the heterogeneity challenge, i.e., the challenge of non ...
Liwei Che +3 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein +7 more
wiley +1 more source
Multimodal Learning With Transformers: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 This paper is accepted by IEEE ...
Peng Xu, Xiatian Zhu, David A. Clifton
openaire +3 more sources