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25 papers · ranked by Valyu relevance

01
BbioRxiv25 Nov 2025
Understanding cortical computation through the lens of joint-embedding predictive architectures
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
85relevance
02
BbioRxiv11 Mar 2026
Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior
Flóra Hann, Cintia Anna Nagy, Zita Olivia Nagy, Dezso Nemeth + 1 more
The ability to build predictive models of the environment fundamentally drives adaptive behavior. Yet, the real-time dynamics of how these internal models are formed and updated remain poorly understood. Conventional methods often rely on indirect, offline measures or noisy motor responses, limiting insight into the…
82relevance
03
AarXiv8 Dec 2025
Machine Learning: Progress and Prospects
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…
82relevance
04
BbioRxiv4 Sep 2026
Predictive learning with local plasticity in excitatory-inhibitory networks
Henrique Reis Aguiar, Matthias H. Hennig
Predictive coding is a powerful normative framework for understanding cortical computation, but it is still an open question how biologically plausible networks with local plasticity support predictive inference and representation learning. In this work we show that a recurrent excitatory-inhibitory circuit with purely…
80relevance
05
AarXiv7 Jan 2026
When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
Gal Fybish, Teo Susnjak
Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to…
79relevance
06
BbioRxiv10 Jan 2026
A normative account of human temporal structure learning
Niloufar Razmi, Xufeng Caesar Dai, Leah Bakst, Matthew R. Nassar
People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers…
78relevance
07
BbioRxiv15 Feb 2026
Predictive E-prop: A biologically inspired approach to train predictive coding-based recurrent spiking neural networks
Davide Noè, Hideaki Yamamoto, Yuichi Katori, Shigeo Sato
The predictive coding framework offers a compelling model for temporal signal processing in the cortex. Recent studies explored its implementation in spiking architectures using Hebbian plasticity rules or offline learning; however, a biologically inspired model that enables gradient-based minimization of prediction…
78relevance
08
BbioRxiv19 Feb 2026
Building Goal-Directed Cognitive Graphs
Adithya Gungi, Pradyumna Sepúlveda Delgado, Ines F. Aitsahalia, Marta Blanco-Pozo + 1 more
Flexible, goal-directed behavior depends on learning predictive relationships, yet how reward shapes learned transition structure remains incompletely understood. Here we introduce the Sparse Cognitive Graph, a reinforcement-learning framework in which a continuously updated transition representation is sparsified into…
77relevance
09
PPubMed28 Apr 2026
Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments
Essa E. Almazroei
The development of digital learning environments has generated rich educational data capable of supporting early prediction of student outcomes. In this study, seven diverse datasets, spanning demographics, parental education, assessment history, and VLE engagement, were integrated into a unified machine-learning…
75relevance
10
PPubMed30 Mar 2026
Forecasting migraine with time-series machine learning from mobile health data
Fahim Faisal, Amalie C. Poole, Antonios Danelakis, Tore Wergeland + 15 more
Background Machine learning provides a powerful framework to model the complex patterns underlying migraine attack onset from real-world high dimensional datasets. In this study, we used machine learning to forecast headache days using mobile health (mHealth) data from a migraine biofeedback treatment app. Method This…
75relevance
11
CchemRxiv17 Oct 2025
Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
73relevance
12
AarXiv21 Oct 2025
Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning
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…
72relevance
13
PPubMed9 Jun 2026
Methodological guidance on clinical prediction models in mental health research
Raquel Costa, Bruno de Sousa, Thomas Kneib, Rui Martins + 1 more
Clinical prediction models play a crucial role in advancing personalized care for mental health disorders, providing essential insights for diagnosis, prognosis and intervention planning. This work examines the current methodological approaches used to develop such models, emphasizing their application to mental health…
70relevance
14
CchemRxiv9 Jan 2026
Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
70relevance
15
CchemRxiv23 Oct 2025
Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition
Authors not listed
Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…
70relevance
16
AarXiv10 May 2026
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
Siyu Wu, Cong Xu, Wei Zhang
Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have boosted predictive accuracy, most models rely on deterministic vector embeddings and opaque latent state transitions, limiting interpretability regarding how specific…
66relevance
17
CchemRxiv6 Jan 2026
Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction
Authors not listed
Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
66relevance
18
PPubMed22 Oct 2025
Explainable machine learning for predicting hospital employees' quality of life using psychosocial work environment data
Arwa Alumran, Bashayer Alshahrani, Nida Aslam, Irfan Ullah Khan + 5 more
Health-Related Quality of Life (HRQL) embodies the impact of an individual's health on their ability to live a fulfilling life. Quality of Life (QoL) is influenced by a range of factors, including physical functioning and wellbeing, psychological functioning, work environment (WE), lifestyle, and social relations.…
66relevance
19
CchemRxiv21 Oct 2025
Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
66relevance
20
PPubMed22 Jun 2026
Predictive coding is not a theory of anticipation
Romain Brette
Anticipation is a hallmark of all living phenomena, from bacteria to humans. If I notice a cloudy sky, I may take an umbrella to avoid getting wet. In cyanobacteria, which are photosynthetic bacteria, the chromosome decompacts shortly before dawn thanks to circadian rhythms, so that gene expression is favored when it…
65relevance
21
CchemRxiv30 Oct 2025
Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
64relevance
22
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
64relevance
23
PPubMed31 Dec 2025
Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review
Hwayoung Cho, Jiyoun Song, Hannah Cho, Lin Li + 5 more
Background More than half of people with HIV are now older than 50 years, and they face an approximately 60% higher risk of developing dementia compared with the general population. In recent years, the application of artificial intelligence, particularly machine learning, combined with the growing availability of…
63relevance
24
CchemRxiv18 Nov 2025
High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds
Authors not listed
This research investigates predicting the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO; short HL) gap of natural compounds, a crucial property for understanding molecular electronic behavior relevant to cheminformatics and materials science. To address the high computational…
61relevance
25
AarXiv13 Oct 2025
A Joint Learning Approach to Hardware Caching and Prefetching
Yuan, Samuel, Saxena, Divyanshu + 6 more
Several learned policies have been proposed to replace heuristics for scheduling, caching, and other system components in modern systems. By leveraging diverse features, learning from historical trends, and predicting future behaviors, such models promise to keep pace with ever-increasing workload dynamism and…
61relevance

Results for predictive learning

Understanding cortical computation through the lens of joint-embedding predictive architectures

Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior

Machine Learning: Progress and Prospects

Predictive learning with local plasticity in excitatory-inhibitory networks

When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

A normative account of human temporal structure learning

Predictive E-prop: A biologically inspired approach to train predictive coding-based recurrent spiking neural networks

Building Goal-Directed Cognitive Graphs

Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments

Forecasting migraine with time-series machine learning from mobile health data

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning

Methodological guidance on clinical prediction models in mental health research

Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning

Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction

Explainable machine learning for predicting hospital employees' quality of life using psychosocial work environment data

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Predictive coding is not a theory of anticipation

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review

High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds

A Joint Learning Approach to Hardware Caching and Prefetching

Understanding cortical computation through the lens of joint-embedding predictive architectures

Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior

Machine Learning: Progress and Prospects

Predictive learning with local plasticity in excitatory-inhibitory networks

When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

A normative account of human temporal structure learning

Predictive E-prop: A biologically inspired approach to train predictive coding-based recurrent spiking neural networks

Building Goal-Directed Cognitive Graphs

Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments

Forecasting migraine with time-series machine learning from mobile health data

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Parametrising the Inhomogeneity Inducing Capacity of a Training Set, and its Impact on Supervised Learning

Methodological guidance on clinical prediction models in mental health research

Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition

Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning

Embedding-Based Applicability Domain for Graph Neural Networks: A Comprehensive Benchmark on Molecular Property Prediction

Explainable machine learning for predicting hospital employees' quality of life using psychosocial work environment data

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Predictive coding is not a theory of anticipation

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review

High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds

A Joint Learning Approach to Hardware Caching and Prefetching