Results 41 to 50 of about 367,129 (284)
Learning Social Image Embedding with Deep Multimodal Attention Networks
, 2017 Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search.
He, Yueying +5 more
core +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
doaj +1 more source
Situating multimodal learning analytics [PDF]
, 2016 The digital age has introduced a host of new challenges and opportunities for the learning sciences community. These challenges and opportunities are particularly abundant in multimodal learning analytics (MMLA), a research methodology that aims to ...
Abrahamson, D +5 more
core +1 more source
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
, 2019 Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combination of modalities, their number of parameters grows quadratically with ...
Ben-younes, Hedi +3 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Although not always achieved, complete chemotherapy‐induced nausea and vomiting (CINV) control is the conventional goal of CINV prophylaxis. In this two‐center, mixed‐methods study, we sought to understand the preferences of adolescent patients and family caregivers for CINV control endpoints.
Haley Newman +8 more
wiley +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
openaire +2 more sources
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
openaire +3 more sources
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
Learning weakly supervised multimodal phoneme embeddings
, 2017 Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e.
Chaabouni, Rahma +3 more
core +3 more sources
Ostensive signals support learning from novel attention cues during infancy [PDF]
, 2014 Social attention cues (e.g., head turning, gaze direction) highlight which events young infants should attend to in a busy environment and, recently, have been shown to shape infants' likelihood of learning about objects and events. Although studies have
Csibra +15 more
core +2 more sources