Results 41 to 50 of about 7,496 (160)
Distral: Robust Multitask Reinforcement Learning
, 2017 Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks.
Teh, YW +7 more
openaire +3 more sources
Fine Tuned Multitasking Neural Network for Parkinson's Disease Detection from Voice Recordings
TecnoLógicasParkinson's disease (PD) is the second most prevalent neurodegenerative disorder in old age. It is characterized by symptoms such as resting tremor, rigidity, and gait disturbances. It also affects the natural production of speech, causing tremors of the
Diego Alexander López-Santander +2 more
doaj +1 more source
Review of Time Series Forecasting Methods Based on Deep Learning
Nanjing Hangkong Hangtian Daxue XuebaoDeep learning has emerged as an effective solution for time series forecasting due to its superior ability to capture complex relationships and patterns within temporal data.
PAN Zhisong, HAN Xiao, LI Wei
doaj +1 more source
Evolutionary Multitask Optimization Based on Search Behavior Learning
IEEE AccessEvolutionary multitask optimization (EMTO) is an emerging topic in evolutionary computation to solve multitask optimization problems (MTOPs) with the help of knowledge transfer (KT).
Dan-Ting Duan +2 more
doaj +1 more source
Cost-Effective Multitask Active Learning in Wearable Sensor Systems
SensorsMultitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations.
Asiful Arefeen, Hassan Ghasemzadeh
doaj +1 more source
Multitask Learning Based on Least Squares Support Vector Regression for Stock Forecast
Axioms, 2022 Various factors make stock market forecasting difficult and arduous. Single-task learning models fail to achieve good results because they ignore the correlation between multiple related tasks. Multitask learning methods can capture the cross-correlation
Heng-Chang Zhang +3 more
doaj +1 more source
Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI
Frontiers in Robotics and AI, 2021 Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided.
Samuel Spaulding +3 more
doaj +1 more source
Geolocation with Attention-Based Multitask Learning Models [PDF]
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), 2019 Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, the classes are geographic areas previously identified; in the second, the models directly predict ...
Fornaciari, Tommaso, Hovy, Dirk
openaire +2 more sources
Adversarial Multitask Learning for Domain Adaptation Through Domain Adapter
IEEE AccessThis study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. The proposed approach addresses the challenges posed
Hidayaturrahman +3 more
doaj +1 more source
Multitask Soft Option Learning
, 2019 Published at UAI ...
Igl, Maximilian (author) +6 more
openaire +3 more sources