Results 31 to 40 of about 3,537 (211)

Personalized Federated Multi-Task Learning over Wireless Fading Channels

Algorithms, 2022
Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task.
Matin Mortaheb, Cemil Vahapoglu, Sennur Ulukus   +2 more
doaj   +1 more source

A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

Applied Sciences, 2021
Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU ...
Suhyune Son, Seonjeong Hwang, Sohyeun Bae, Soo Jun Park, Jang-Hwan Choi   +4 more
doaj   +1 more source

MATTE: Multi-task multi-scale attention [PDF]

, 2023
In this work, we propose a general method for learning task and scale based attention representations in Multi-Task Learning (MTL) for vision. It relies on learning and maintaining cross-task and cross-scale representations of visual information, whose ...
Worring, M.; id_orcid, van Noord, N.; id_orcid, Strezoski, G.   +2 more
core   +1 more source

Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection

BMC Genomics, 2008
Background Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely ...
Yang Mary, Meng Hao-Hua, Li Guo-Zheng, Yang Jack Y, Deng Youping   +4 more
doaj   +1 more source

MTL-UE: Learning to Learn Nothing for Multi-Task Learning

CoRR
Accepted by ICML ...
Yi Yu 0011, Song Xia, Siyuan Yang 0001, Chenqi Kong, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot   +7 more
openaire   +3 more sources

Bidirectional Domain Adaptation Using Weighted Multi-Task Learning

, 2021
Domain adaption in syntactic parsing is still a significant challenge. We address the issue of data imbalance between the in-domain and out-of-domain treebank typically used for the problem.
Daniel Dakota, Sayyed, Zeeshan Ali,, Zeeshan Ali Sayyed, Dakota, Daniel,, Kuebler, Sandra,, Sandra Kübler   +5 more
core   +1 more source

FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2020
Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and ...
Di Yang 0002, Ningjia Qiu, Peng Wang, Huamin Yang   +3 more
openaire   +1 more source

Task-Aware Dynamic Model Optimization for Multi-Task Learning

IEEE Access, 2023
Multi-task learning (MTL) is a field in which a deep neural network simultaneously learns knowledge from multiple tasks. However, achieving resource-efficient MTL remains challenging due to entangled network parameters across tasks and varying task ...
Sujin Choi, Hyundong Jin, Eunwoo Kim
doaj   +1 more source

MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. However, in multi-task learning (MTL) scenarios, LoRA tends to obscure the distinction between tasks by projecting sparse high-dimensional features from different tasks into ...
Yaming Yang 0001, Dilxat Muhtar, Yelong Shen, Yuefeng Zhan, Jianfeng Liu, Yujing Wang 0002, Hao Sun 0015, Denvy Deng, Feng Sun 0008, Qi Zhang 0066, Weizhu Chen, Yunhai Tong   +11 more
openaire   +2 more sources

Guitar Chord Sensing and Recognition Using Multi-Task Learning and Physical Data Augmentation with Robotics

Sensors, 2020
In recent years, many researchers have shown increasing interest in music information retrieval (MIR) applications, with automatic chord recognition being one of the popular tasks.
Gerelmaa Byambatsogt, Lodoiravsal Choimaa, Gou Koutaki   +2 more
doaj   +1 more source