Results 51 to 60 of about 1,057,004 (267)
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Pediatric bone sarcoma patients and survivors may experience psychosocial challenges related to childhood cancer after their intensive, body‐altering treatment. This cross‐sectional study aimed to evaluate generic and survivor‐specific psychosocial outcomes in a national cohort of pediatric bone sarcoma patients and survivors, and ...
Hinke van der Hoek +14 more
wiley +1 more source
Lite‐weight semantic segmentation with AG self‐attention
IET Computer VisionDue to the large computational and GPUs memory cost of semantic segmentation, some works focus on designing a lite weight model to achieve a good trade‐off between computational cost and accuracy. A common method is to combined CNN and vision transformer.
Bing Liu +4 more
doaj +1 more source
Self-attentive Biaffine Dependency Parsing [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 The current state-of-the-art dependency parsing approaches employ BiLSTMs to encode input sentences.Motivated by the success of the transformer-based machine translation, this work for the first time applies the self-attention mechanism to dependency parsing as the replacement of the BiLSTM-based encoders, leading to competitive performance on both ...
Ying Li 0065 +5 more
openaire +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Adolescents with haematological malignancies face significant emotional and relational challenges, often accompanied by difficulties in communicating their needs within the healthcare context. To address these issues, a narrative‐based psycho‐educational intervention based on the creation and prescription of Ironic Medications was ...
Marta Stoppa +7 more
wiley +1 more source
Centered Self-attention Layers
The self-attention mechanism in transformers and the message-passing mechanism in graph neural networks are repeatedly applied within deep learning architectures. We show that this application inevitably leads to oversmoothing, i.e., to similar representations at the deeper layers for different tokens in transformers and different nodes in graph neural
Ameen Ali, Tomer Galanti, Lior Wolf
openaire +2 more sources
Unveiling Vulnerability of Self-Attention
Proceedings of the Language Resources and Evaluation ConferencePre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipulating word inputs, they are limited by their means of generating adversarial samples, lacking generalization to versatile real-world attack.
Khai Jiet Liong +2 more
openaire +3 more sources
A Self-Attentive model for Knowledge Tracing
CoRR, 2019 International Conference on Education Data ...
Shalini Pandey, George Karypis
openaire +3 more sources
Self-Attention for Audio Super-Resolution [PDF]
2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021 MLSP ...
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Medication nonadherence during the first 100 days after pediatric hematopoietic stem cell transplantation (HSCT) and during oncology treatment increases risk for complications. BMT4me is a caregiver‐facing mobile health (mHealth) application providing medication reminders, symptom tracking, and note‐taking features to support ...
Micah A. Skeens +4 more
wiley +1 more source
Adopting Attention and Cross-Layer Features for Fine-Grained Representation
IEEE Access, 2022 Fine-grained visual classification (FGVC) is challenging task due to discriminative feature representations. The attention-based methods show great potential for FGVC, which neglect that the deeply digging inter-layer feature relations have an impact on ...
fayou Sun, Hea Choon Ngo, Yong Wee Sek
doaj +1 more source