23 papers · ranked by Valyu relevance
Giovanni Cavallanti, Nicolò Cesa‐Bianchi
We investigate online kernel algorithms which simultaneously process multiple classification tasks while a fixed constraint is imposed on the size of their active sets. We focus in particular on the design of algorithms that can efficiently deal with problems where the number of tasks is extremely high and the task…
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…
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…
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…
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…
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…
Christian Widmer, Nora C Toussaint, Yasemin Altun, Gunnar Rätsch
Background The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In…
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…
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.…
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…
Parya Aghasafari, Pei-Chi Yang, Divya C. Kernik, Kauho Sakamoto + 4 more
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability and…
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…
Elena L. Cáceres, Nicholas C. Mew, Michael J. Keiser
Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological datasets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios whose characteristics differ from a random split of conventional…
Meng Joo Er, Rajasekar Venkatesan, Ning Wang
—Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are subcategories of…
Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama + 1 more
'Shiqian Wu'] Abstract – In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class…
Jonathan Fine, Anand Rasjashekar, Gaurav Chopra
We present a deep learning method for identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra without the use of any database, pre-established rules, procedures, or peak-matching methods. We derive patterns and correlations directly from spectral data representing multiple…
Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama
—In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In…
Antonios Vogiatzis, Stavros Orfanoudakis, Georgios Chalkiadakis, Konstantia Moirogiorgou + 2 more
'Konstantia Moirogiorgou' 'Michalis Zervakis' 'Loris Nanni'] Multiclass image classification is a complex task that has been thoroughly investigated in the past. Decomposition-based strategies are commonly employed to address it. Typically, these methods divide the original problem into smaller, potentially simpler…
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…
Fei Deng, Jibing Huang, Xiaoling Yuan, Chao Cheng + 1 more
Most of the biomedical datasets, including those of ‘omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine…
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…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Chi Zhang, Dmytro Antypov, Matthew J Rosseinsky, Matthew Stephen Dyer
Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class…