Results 21 to 30 of about 1,489,144 (280)
Sign-Regularized Multi-Task Learning
, 2023 17 pages, 4 figures ...
Torres, Johnny +5 more
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Robust multi-task feature learning [PDF]
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012 Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning algorithms have received increasing attention and they have been successfully applied to many applications involving high-dimensional data.
Pinghua, Gong +2 more
openaire +2 more sources
Multi-Task Reinforcement Learning in Humans [PDF]
Nature Human Behaviour, 2019 ABSTRACTThe ability to transfer knowledge across tasks and generalize to novel ones is an important hallmark of human intelligence. Yet not much is known about human multi-task reinforcement learning. We study participants’ behavior in a novel two-step decision making task with multiple features and changing reward functions.
Momchil S. Tomov +2 more
openaire +4 more sources
A multi-task learning CNN for image steganalysis [PDF]
, 2018 Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether ...
Li, Chang-Tsun +4 more
core +1 more source
Few-Shot and Zero-Shot Learning for Historical Text Normalization [PDF]
, 2019 Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different ...
Bollmann, Marcel +2 more
core +3 more sources
Improving Persian Named Entity Recognition Through Multi Task Learning
International Journal of Information and Communication Technology Research, 2021 Named Entity Recognition is a challenging task, specially for low resource languages, such as Persian, due to the lack of massive gold data. As developing manually-annotated datasets is time consuming and expensive, we use a multitask learning (MTL ...
Mohammad Hadi Bokaei +2 more
doaj
Multi‐task learning for captioning images with novel words
IET Computer Vision, 2019 Recent captioning models are limited in their ability to describe concepts unseen in paired image–sentence pairs. This study presents a framework of multi‐task learning for describing novel words not present in existing image‐captioning datasets.
He Zheng +4 more
doaj +1 more source
Design of Efficient Speech Emotion Recognition Based on Multi Task Learning
IEEE Access, 2023 Speech emotion recognition technology includes feature extraction and classifier construction. However, the recognition efficiency is reduced due to noise interference and gender differences. To solve this problem, this paper used two multi-task learning
Liu Yunxiang, Zhang Kexin
doaj +1 more source
Task-Projected Hyperdimensional Computing for Multi-Task Learning
, 2020 Brain-inspired Hyperdimensional (HD) computing is an emerging technique for cognitive tasks in the field of low-power design. As a fast-learning and energy-efficient computational paradigm, HD computing has shown great success in many real-world ...
A Rahimi +6 more
core +1 more source
A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks
IEEE Access, 2019 With the success of deep learning in a wide variety of areas, many deep multi-task learning (MTL) models have been proposed claiming improvements in performance obtained by sharing the learned structure across several related tasks. However, the dynamics
Ting Gong +7 more
doaj +1 more source