Results 51 to 60 of about 7,496 (160)

Hierarchical Multitask Learning With CTC [PDF]

2018 IEEE Spoken Language Technology Workshop (SLT), 2018
In Automatic Speech Recognition, it is still challenging to learn useful intermediate representations when using high-level (or abstract) target units such as words. For that reason, when only a few hundreds of hours of training data are available, character or phoneme-based systems tend to outperform word-based systems.
Ramon Sanabria, Florian Metze
openaire   +1 more source

Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders

Frontiers in Psychiatry
Multimodal Multitask Federated Foundation Models (M3T-FedFMs) represent a new frontier in artificial intelligence (AI), enabling integration of diverse data modalities and multitask learning while preserving data confidentiality through federated ...
Maryam Ebrahimi, Rajeev Sahay, Bita Akram, Bita Akram, Seyyedali Hosseinalipour, Seyyedali Hosseinalipour   +5 more
doaj   +1 more source

Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

Applied Sciences, 2019
Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based ...
Jianliang Yang, Yuenan Liu, Minghui Qian, Chenghua Guan, Xiangfei Yuan   +4 more
doaj   +1 more source

Multitask Learning with Local Attention for Tibetan Speech Recognition

Complexity, 2020
In this paper, we propose to incorporate the local attention in WaveNet-CTC to improve the performance of Tibetan speech recognition in multitask learning.
Hui Wang, Fei Gao, Yue Zhao, Li Yang, Jianjian Yue, Huilin Ma   +5 more
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Multitask Learning Strengthens Adversarial Robustness [PDF]

, 2020
Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network. We present both theoretical and empirical analyses that connect the adversarial robustness of a model to the number of tasks that it is trained on.
Mao, Chengzhi, Gupta, Amogh, Nitin, Vikram, Ray, Baishakhi, Song, Shuran, Yang, Junfeng, Vondrick, Carl   +6 more
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Collaborative Estimation of Tropical Cyclone Wind Radii With Multitask Learning and Multiteacher Distillation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Wind radii provide a quantitative measure of the structural characteristics of tropical cyclones (TCs), and accurate estimation of wind radii is crucial for disaster prevention and emergency response.
Jia Liu, Yinlei Yue, Yongjun Jin, Kaijun Ren, Xiang Wang, Kefeng Deng, Chongjiu Deng, Ke Deng   +7 more
doaj   +1 more source

When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework

Remote Sensing, 2020
In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images.
Zhicheng Zhao, Ze Luo, Jian Li, Can Chen, Yingchao Piao   +4 more
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A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata

Scientific Reports
Acute ischemic stroke is a major global health challenge, leading to long-term disability or death without timely intervention. Among neuroimaging modalities, spatio-temporal (4D) computed tomography perfusion (CTP) is widely used to assess cerebral ...
Kimberly Amador, Anthony J. Winder, Jens Fiehler, Philip A. Barber, Matthias Wilms, Nils D. Forkert   +5 more
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Improving mental dysfunction detection from EEG signals: Self-contrastive learning and multitask learning with transformers

Alexandria Engineering Journal
Existing works relied on subjective interviews in clinical settings, which does not detect mental health problems at early stages. With the advancement in learning strategies, research are trying to find a better way for detecting mental disorders and ...
Shakila Basheer, Ghadah Aldehim, Ala Saleh Alluhaidan, Sapiah Sakri   +3 more
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Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning

Journal of Cheminformatics
The cytochrome P450 (CYP) superfamily metabolises a wide range of compounds; however, drug-induced CYP inhibition can lead to adverse interactions. Identifying potential CYP inhibitors is crucial for safe drug administration.
Elpri Eka Permadi, Reiko Watanabe, Kenji Mizuguchi   +2 more
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