27 papers · ranked by Valyu relevance
Stephen B Lee, Alexis B Carter, Muhammad Hamis Haider, Seok-Bum Ko + 2 more
Artificial intelligence (AI) is already fundamentally changing society, with medicine being no exception. AI will impact how we practice, how hospitals operate, and even the practice of medicine itself. The use of AI-based products has already begun, with examples including AI scribes and large language models such as…
Makrehchi, Masoud
We analyze a reversed-supervision strategy that searches over labelings of a large unlabeled set B to minimize error on a small labeled set A. The search space is 2 n , and the resulting complexity remains exponential even under large constant-factor speedups (e.g., quantum or massively parallel hardware).…
Ahmed Mehedi Nizam, Zeheng Wang
We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class’s existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incremental calculations in…
Julia Westermayr, P. Marquetand
Spectroscopy, the exploration of matter through its interaction with electromagnetic radiation, is relevant in many diverse research fields, such as biology, materials science, medicine, and chemistry, and enables the qualitative and quantitative characterization of samples. Machine learning has revolutionized…
Gargi Roy, Dalia Chakrabarty
We introduce parametrisation of that property of the available training dataset, that necessitates an inhomogeneous correlation structure for the function that is learnt as a model of the relationship between the pair of variables, observations of which comprise the considered training data. We refer to a…
Dongshan Lin, Zhenyue Wang, Jiaqi Liao, Nan Li + 2 more
The growing diversity of anthropogenic chemicals in the environment far exceeds the scope of routine analytical monitoring. Non-target screening (NTS) using high-resolution mass spectrometry (HRMS) has thus emerged to discover unknown organic contaminants. Liquid or gas chromatography (LC/GC) coupled with ion…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Malik A. Hussain, Md Imran H. Khan, Azharul Karim, Mircea Oroian
The continued evolution and advances in Artificial Intelligence (AI) technologies are offering innovative solutions and setting the futuristic trends in the food sector. The use of different Machine Learning (ML)-based models has demonstrated promising applications in the food processing industry. Processing operations…
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The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Usman Khalid, Mehmet Kaya, Reda Alhajj, Gaetano Nucifora + 1 more
The Background/Objectives: The excessive dependence on data annotation, the lack of labeled data, and the substantial expense of data annotation, especially in healthcare, have constrained the efficacy of conventional supervised learning methodologies. Self-supervised learning (SSL) has arisen as a viable option by…
Meftahul Jannat, Md Shahab Uddin, Mohammad Asif Hasan, Md Saimun Alam + 3 more
Introduction Timely detection of jute leaf diseases is vital for sustaining crop health and farmer livelihoods. Existing deep learning approaches often rely on large, annotated datasets, which are costly and time-consuming to produce. Methods and results To address this challenge, a lightweight convolutional neural…
David Mendez, Fernando Martin-Maroto, Gonzalo G. de Polavieja
Symbolic methods are generally not considered competitive with strong modern learners on realistic supervised tasks. We evaluate Algebraic Machine Learning (AML), a framework that learns through subdirect decomposition of algebraic structure rather than numerical optimization, against standard baselines on image and…
Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun Zou
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis testing: if a neural network can distinguish, for example, an image of a handwritten digit from another, can it also distinguish an "image of…
Sebastian Medina, Eduardo Romero, Angel Cruz-Roa, Fabio A. González + 1 more
Classification methods based on deep learning require selecting between fully-supervised or weakly-supervised approaches, each presenting limitations in uncertainty quantification and interpretability. A framework unifying both supervision modes while maintaining quantifiable interpretation metrics remains unexplored.…
Fanxiao Wani Qiu, Oscar Leong
Understanding how humans and machines learn from sparse data is central to cognitive science and machine learning. Using a species-fair design, we compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised category learning task. Both learners are exposed to novel object categories under…
David Harding-Larsen, Brianna Marie Lax, Martina Escorial García, Catarina Mendonça + 3 more
Machine learning has repeatedly shown the ability to accelerate protein engineering, but many approaches demand large amounts of robust, high-quality training data as well as substantial computational expertise. While large pre-trained models can function as zero-shot proxies for predicting variant effects, selecting…
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We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
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High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Gammerman, Alexander
This Inaugural Lecture was given at Royal Holloway University of London in 1996. It covers an introduction to machine learning and describes various theoretical advances and practical projects in the field. The Lecture here is presented in its original format, but a few remarks have been added in 2025 to reflect recent…
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Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Mo Zhou, Emily Schwartz, Arish Alreja, R. Mark Richardson + 2 more
Deep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key…
Aman Yadav, Arlin Birkby, Noah Armstrong, Assame Arnob + 9 more
Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among…
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Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
Yue Lyu, Steven Hsesheng Lin, Xuelin Huang, Ziyi Li
This paper introduces SuperSurv, a user-friendly R package for building, evaluating, and interpreting ensemble models for right-censored survival data. Although many survival modeling methods are available, existing tools are often model-specific and lack a unified platform for systematically integrating, comparing…
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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…
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Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Marium H. Alvi, Ryley P. Nathaniel, Karmen Rai, Liya Ma
Adapting behaviour when reward contingencies change is a core function of cognitive control, but the underlying trial-by-trial computations are hard to observe when tasks cue each rule or allow only one switch per session. We developed the Feature-Rule Switching Task (FRST), in which common marmosets (Callithrix…