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20 papers · ranked by Valyu relevance

01
AarXiv2022
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis, Kleanthis Malialis + 2 more
'Kleanthis Malialis' 'Δημήτριος Γ. Ηλιάδης' 'Theocharis Theocharides'] Please cite as follows: Papatheodoulou, D., Pavlou, P., Vrachimis, S.G., Malialis, K., Eliades, D.G., Theocharides, T. (2022). A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring. In: Maglogiannis, I.…
86relevance
02
PPubMed22 Dec 2021
Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification
Weiping Zheng, Zhenyao Mo, Gansen Zhao, Iren E. Kuznetsova
Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, the similarity relations amongst scenes are correlated with the…
82relevance
03
PPubMed3 Jun 2022
Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations
Limei Song, Yudan Ren, Yuqing Hou, Xiaowei He + 1 more
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to induce functional brain activities corresponding to various cognitive tasks. A relatively under-explored question is whether there exist fundamental differences in fMRI signal composition patterns that can effectively classify the task…
80relevance
04
BbioRxiv30 Jun 2023
Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection
Kim Bjerge, Quentin Geissmann, Jamie Alison, Hjalte M. R. Mann + 3 more
Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology and monitoring of biodiversity. However, a major challenge is the diversity of insects and close resemblances of many species combined with computer vision are…
79relevance
05
AarXiv18 Dec 2024
The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling
Mauro Andrés Nievas Offidani, Facundo Roffet, Claudio Delrieux, Maria Carolina Gonzalez Galtier + 1 more
classifiers through problem transformation, ontology engineering, and model ensembling Authors: ['Mauro Andrés Nievas Offidani' 'Facundo Roffet' 'Claudio Delrieux' 'Maria Carolina Gonzalez Galtier' 'Marcos Zárate'] Abstract. Classification is a fundamental task in machine learning. While conventional methods—such as…
78relevance
06
BbioRxiv26 Nov 2022
Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways
Willem A.M. Wybo, Matthias C. Tsai, Viet Anh Khoa Tran, Bernd Illing + 3 more
While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we first demonstrate that thin dendritic branches are…
77relevance
07
AarXiv23 Jul 2024
A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset
Koushikey Chhapariya, Alexandre Benoît, Krishna Mohan Buddhiraju, Anil Kumar
Hyperspectral Images: Application to the large-scale dataset Authors: ['Koushikey Chhapariya' 'Alexandre Benoît' 'Krishna Mohan Buddhiraju' 'Anil Kumar'] Abstract—Multitask learning is a widely recognized technique in the field of computer vision and deep learning domain. However, it is still a research question in…
76relevance
08
AarXiv2022
Design Perspectives of Multitask Deep Learning Models and Applications
Yeshwant Singh, Anupam Biswas, Angshuman Bora, Debashish Malakar + 2 more
'Subham Chakraborty' 'Suman Bera'] Abstract- In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under…
75relevance
09
PPubMed6 May 2022
A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease
Solale Tabarestani, Mohammad Eslami, Mercedes Cabrerizo, Rosie E. Curiel + 7 more
'Rosie E. Curiel' 'Armando Barreto' 'Naphtali Rishe' 'David Vaillancourt' 'Steven T. DeKosky' 'David A. Loewenstein' 'Ranjan Duara' 'Malek Adjouadi'] With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through…
75relevance
10
PPubMed3 Oct 2023
A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors
Muhammad Adeel Nisar, Kimiaki Shirahama, Muhammad Tausif Irshad, Xinyu Huang + 2 more
'Xinyu Huang' 'Marcin Grzegorzek' 'Carlos M. Travieso-González'] Machine learning with deep neural networks (DNNs) is widely used for human activity recognition (HAR) to automatically learn features, identify and analyze activities, and to produce a consequential outcome in numerous applications. However, learning…
74relevance
11
BbioRxiv14 Oct 2025
Structured Chemical Reaction Modeling with Multitask Graph Neural Networks
Maryam Astero, Anchen Li, Elena Casiraghi, Juho Rousu
Modeling chemical reactions requires connecting fine-grained atom–bond edits with broader semantic categories. Yet, most machine learning approaches model these aspects in isolation: atom mapping, reaction center identification, and reaction classification are treated as separate problems. This separation limits…
74relevance
12
PPubMed28 Feb 2025
Cost-Effective Multitask Active Learning in Wearable Sensor Systems
Asiful Arefeen, Hassan Ghasemzadeh, Bashir Morshed
Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. Leveraging inductive knowledge transfer, these models mitigate the risk of overfitting on any specific task, leading to enhanced overall performance.…
73relevance
13
BbioRxiv13 May 2024
Improving Drug Sensitivity Prediction and Inference by Multi-Task Learning
Jared Strauch, Amir Asiaee
The development of models to predict sensitivity to anticancer drugs is an area of significant interest, given the diverse responses to treatment among patients and the considerable expense and time involved in anticancer drug development. Leveraging “omic” data and anticancer response information from the Cancer Cell…
72relevance
14
AarXiv21 Apr 2022
Comprehensive comparative study of multi-label classification methods
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
—Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods. However, they are limited in the number of methods and datasets considered. This work…
69relevance
15
CchemRxiv27 Sep 2023
Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields
Leif Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz + 1 more
Transferable neural network potentials have shown great promise as an avenue to increase the accuracy and applicability of existing atomistic force fields for organic molecules and inorganic materials. Training sets used to develop transferable potentials are very large, typically millions of examples, and as such, are…
68relevance
16
BbioRxiv25 Aug 2022
Model Choice Metrics to Optimize Profile-QSAR Performance
Stewart He, Sookyung Kim, Kevin S. McLoughlin, Hiranmayi Ranganathan + 2 more
Predicting molecular activity against protein targets is difficult because of the paucity of experimental data. Approaches like multitask modeling and collaborative filtering seek to improve model accuracy by leveraging results from multiple targets, but are limited because different compounds are measured with…
67relevance
17
AarXiv2022
A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification
Pengfei Gao, Lai Dedi, Lijiao Zhao, Yue Liang + 1 more
As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exploit the underlying dependency in the label space, and often suffer from the problems of poorly…
66relevance
18
BbioRxiv1 Jan 2025
Multivariate Bayesian Inversion for Classification and Regression
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
57relevance
19
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
53relevance
20
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
52relevance

Results for Multitask classification

A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling

Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset

Design Perspectives of Multitask Deep Learning Models and Applications

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease

A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors

Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Cost-Effective Multitask Active Learning in Wearable Sensor Systems

Improving Drug Sensitivity Prediction and Inference by Multi-Task Learning

Comprehensive comparative study of multi-label classification methods

Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields

Model Choice Metrics to Optimize Profile-QSAR Performance

A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification

Multivariate Bayesian Inversion for Classification and Regression

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling

Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset

Design Perspectives of Multitask Deep Learning Models and Applications

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease

A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors

Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Cost-Effective Multitask Active Learning in Wearable Sensor Systems

Improving Drug Sensitivity Prediction and Inference by Multi-Task Learning

Comprehensive comparative study of multi-label classification methods

Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields

Model Choice Metrics to Optimize Profile-QSAR Performance

A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification

Multivariate Bayesian Inversion for Classification and Regression

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis