Results 111 to 120 of about 3,537 (211)

Multi-task model and feature joint learning

, 2015
Given several tasks, multi-task learning (MTL) learns multiple tasks jointly by exploring the interdependence between them. The basic assumption in MTL is that those tasks are indeed related.
Tian, X, Liu, T, Li, Y, Tao, D
core  

Dual-scale BERT using multi-trait representations for holistic and trait-specific essay grading

ETRI Journal
As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling ...
Minsoo Cho, Jin-Xia Huang, Oh-Woog Kwon
doaj   +1 more source

A Multi-Task Learning Framework with Enhanced Cross-Level Semantic Consistency for Multi-Level Land Cover Classification

Remote Sensing
The multi-scale characteristics of remote sensing imagery have an inherent correspondence with the hierarchical structure of land cover classification systems, providing a theoretical foundation for multi-level land cover classification.
Shilin Tao, Haoyu Fu, Ruiqi Yang, Leiguang Wang   +3 more
doaj   +1 more source

LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control

Multi-task learning (MTL) has been widely adopted for its ability to simultaneously learn multiple tasks. While existing gradient manipulation methods often yield more balanced solutions than simple scalarization-based approaches, they typically incur a significant computational overhead of $\mathcal{O}(K)$ in both time and memory, where $K$ is the ...
Xiao, Peiyao, Dong, Chaosheng, Zou, Shaofeng, Ji, Kaiyi   +3 more
openaire   +2 more sources

ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks

CoRR
Cross-subject variability in EEG degrades performance of current deep learning models, limiting the development of brain-computer interface (BCI). This paper proposes ISAM-MTL, which is a multi-task learning (MTL) EEG classification model based on identifiable spiking (IS) representations and associative memory (AM) networks.
Junyan Li, Bin Hu 0008, Zhi-Hong Guan
openaire   +2 more sources

Multi-Task Learning as a Bargaining Game

, 2022
In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Joint training reduces computation costs and improves data efficiency; however, since the gradients of these different tasks may conflict ...
Kawaguchi, Kenji, Achituve, Idan, Shamsian, Aviv, Chechik, Gal, Fetaya, Ethan, Navon, Aviv, Maron, Haggai   +6 more
core  

Sharing to Learn and Learning to Share : Meta-learning to Enhance Multi-task Learning

Multi-Task Learning (MTL) enables simultaneous learning of multiple tasks in a shared framework following the principle of ‘sharing to learn,’ to improve the performance of all the tasks.
Upadhyay, Richa
core   +1 more source

Aggregation and Pruning for Continuous Incremental Multi-Task Inference

Mathematics
In resource-constrained mobile systems, efficiently handling incrementally added tasks under dynamically evolving requirements is a critical challenge.
Lining Li, Fenglin Cen, Quan Feng, Ji Xu
doaj   +1 more source

Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

IEEE Access
Split computing has emerged as a promising approach to alleviate the resource constraints of IoT devices by offloading computation to edge servers. However, conventional split computing schemes fail to effectively support multi-task learning (MTL) models,
Haneul Ko, Sangwon Seo, Sangheon Pack
doaj   +1 more source

Real-time facial recognition via multitask learning on raspberry Pi

Scientific Reports
This paper investigates the feasibility of multi-task learning (MTL) for facial recognition on the Raspberry Pi, a low-cost single-board computer, demonstrating its ability to perform complex deep learning tasks in real time. Using MobileNet, MobileNetV2,
Abdulatif Ahmed Ali Aboluhom, Ismet Kandilli   +1 more
doaj   +1 more source