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23 papers · ranked by Valyu relevance

01
PPubMed20 Dec 2022
Novel Meta-Learning Techniques for the Multiclass Image Classification Problem
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…
91relevance
02
PPubMed18 Feb 2019
Mixture of Experts with Entropic Regularization for Data Classification
Billy Peralta, Ariel Saavedra, Luis Caro, Alvaro Soto
Today, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition. “Mixture-of-experts” is a well-known classification technique; it is a probabilistic model consisting of local expert classifiers…
88relevance
03
BbioRxiv17 Sep 2025
Personalized polygenic risk prediction and assessment with a Mixture-of-Experts framework
Shadi Zabad, Yue Li, Simon Gravel
With the increasing availability of high quality genomic data from diverse cohorts, polygenic scores (PRS) have become a mainstay of genetic analyses of complex traits and diseases. Despite their proliferation in numerous research domains, a major obstacle to wider adoption in clinical settings has been the…
88relevance
04
BbioRxiv8 Aug 2024
Mixture-of-Experts Approach for Enhanced Drug-Target Interaction Prediction and Confidence Assessment
Yijingxiu Lu, Sangseon Lee, Soosung Kang, Sun Kim
In recent years, numerous deep learning models have been developed for drug-target interaction (DTI) prediction. These DTI models specialize in handling data with distinct distributions and features, often yielding inconsistent predictions when applied to unseen data points. This inconsistency poses a challenge for…
87relevance
05
CchemRxiv26 Nov 2025
Mixture-of-Experts Transformers for Faithfully Deorbitalized Meta-GGA Density Functionals
Authors not listed
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…
86relevance
06
BbioRxiv1 Jan 2025
GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics
Yuxi Liu, Zhenhao Zhang, Mufan Qiu, Song Wang + 5 more
Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an…
86relevance
07
AarXiv2021
Mixture of ELM based experts with trainable gating network
Laleh Armi, Elham Abbasi, Jamal Zarepour-Ahmadabadi
In this paper, we propose an ensemble learning method based on mixture of experts which is named mixture of ELM based experts with trainable gating network (MEETG) to improve the computing cost and to speed up the learning process of ME. The structure of ME consists of multi layer perceptrons (MLPs) as base experts and…
85relevance
08
AarXiv29 May 2014
Simultaneous Feature and Expert Selection within Mixture of Experts
Billy Peralta
A useful strategy to deal with complex classification scenarios is the "divide and conquer" approach. The mixture of experts (MOE) technique makes use of this strategy by joinly training a set of classifiers, or experts, that are specialized in different regions of the input space. A global model, or gate function…
84relevance
09
PPubMed21 Dec 2007
A mixture of feature experts approach for protein-protein interaction prediction
Yanjun Qi, Judith Klein-Seetharaman, Ziv Bar-Joseph
Background High-throughput methods can directly detect the set of interacting proteins in model species but the results are often incomplete and exhibit high false positive and false negative rates. A number of researchers have recently presented methods for integrating direct and indirect data for predicting…
84relevance
10
AarXiv24 Jun 2024
Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction
Bruce Rushing
Construction Authors: ['Bruce Rushing'] Mixture of experts is a prediction aggregation method in machine learning that aggregates the predictions of specialized experts. This method often outperforms Bayesian methods despite the Bayesian having stronger inductive guarantees. We argue that this is due to the greater…
84relevance
11
PPubMed26 Oct 2010
Infinite mixture-of-experts model for sparse survival regression with application to breast cancer
Sudhir Raman, Thomas J Fuchs, Peter J Wild, Edgar Dahl + 2 more
Background We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is…
84relevance
12
PPubMed29 Apr 2009
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling
Cornelia Caragea, Jivko Sinapov, Drena Dobbs, Vasant Honavar
Background Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is…
84relevance
13
PPubMed6 Jun 2022
A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping Courbariaux et al. Screening Genetic Associations for Subtyping
Marie Courbariaux, Kylliann De Santiago, Cyril Dalmasso, Fabrice Danjou + 5 more
'Fabrice Danjou' 'Samir Bekadar' 'Jean-Christophe Corvol' 'Maria Martinez' 'Marie Szafranski' 'Christophe Ambroise'] Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability…
83relevance
14
BbioRxiv12 Aug 2025
Scaling Molecular Representation Learning with Hierarchical Mixture-of-Experts
Xiang Zhang, Shenbao Yu, Jie Xia, Fan Yang
Recent advancements in large-scale self-supervised pretraining have significantly improved molecular representation learning, yet challenges persist, particularly when addressing distributional shifts (e.g., under scaffold-split). Drawing inspiration from the success of Mixture-of-Experts (MoE) networks in NLP, we…
81relevance
15
AarXiv2015
Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth
Hamidreza Farhidzadeh
- One of the challenging problems in biology is to classify plants based on their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so interesting, because its genetic structure has some similarities with that of human beings. Biologists classify the type of this plant to mutated and not mutated…
80relevance
16
BbioRxiv21 Dec 2024
dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts
Tingting Chen, Hongming Li, Hao Zheng, Yong Fan
Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in imaging neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great…
80relevance
17
AarXiv30 Jan 2024
Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models
Jessica Leoni, Valentina Breschi, Simone Formentin, Mara Tanelli
effective blending of grey and black-box models Authors: ['Jessica Leoni' 'Valentina Breschi' 'Simone Formentin' 'Mara Tanelli'] Traditional models grounded in first principles often struggle with accuracy as the system's complexity increases. Conversely, machine learning approaches, while powerful, face challenges in…
79relevance
18
AarXiv14 Aug 2018
Online aggregation of unbounded losses using shifting experts with confidence
Vladimir V. V’yugin, В. Г. Трунов
We develop the setting of sequential prediction based on shifting experts and on a "smooth" version of the method of specialized experts. To aggregate experts predictions, we use the AdaHedge algorithm, which is a version of the Hedge algorithm with adaptive learning rate, and extend it by the meta-algorithm Fixed…
74relevance
19
CchemRxiv5 Jan 2026
Beyond Deep Learning Dominance: A Scaffold-Aware Hybrid Framework for Robust Toxicity Prediction in Data-Scarce Regimes
Authors not listed
Computational toxicology plays a pivotal role in modern drug discovery and environmental risk assessment; however, the reliability of predictive models on unseen chemical scaffolds remains a critical bottleneck. Deep learning architectures, despite their prevalence, are susceptible to ’silent failures’—yielding…
74relevance
20
AarXiv2014
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
Yanshuai Cao, David J. Fleet
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through…
73relevance
21
AarXiv13 Jun 2024
General Bayesian Predictive Synthesis
Masahiro Kato
This study investigates Bayesian ensemble learning for improving the quality of decision-making. We consider a decision-maker who selects an action from a set of candidates based on a policy trained using observations. In our setting, we assume the existence of experts who provide predictive distributions based on…
63relevance
22
CchemRxiv11 Aug 2025
A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data
Authors not listed
The accurate prediction of fuel mixture properties is essential for the development of alternative fuels, yet remains challenging under data-scarce conditions due to the combinatorial complexity of multi-component systems. In this study, we present a systematic evaluation of three machine learning (ML)…
58relevance
23
CchemRxiv8 Aug 2024
Human-in-the-loop active learning for goal-oriented molecule generation
Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen + 5 more
Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug…
54relevance

Results for mixture-of-experts

Novel Meta-Learning Techniques for the Multiclass Image Classification Problem

Mixture of Experts with Entropic Regularization for Data Classification

Personalized polygenic risk prediction and assessment with a Mixture-of-Experts framework

Mixture-of-Experts Approach for Enhanced Drug-Target Interaction Prediction and Confidence Assessment

Mixture-of-Experts Transformers for Faithfully Deorbitalized Meta-GGA Density Functionals

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Mixture of ELM based experts with trainable gating network

Simultaneous Feature and Expert Selection within Mixture of Experts

A mixture of feature experts approach for protein-protein interaction prediction

Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping Courbariaux et al. Screening Genetic Associations for Subtyping

Scaling Molecular Representation Learning with Hierarchical Mixture-of-Experts

Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models

Online aggregation of unbounded losses using shifting experts with confidence

Beyond Deep Learning Dominance: A Scaffold-Aware Hybrid Framework for Robust Toxicity Prediction in Data-Scarce Regimes

Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

General Bayesian Predictive Synthesis

A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data

Human-in-the-loop active learning for goal-oriented molecule generation

Novel Meta-Learning Techniques for the Multiclass Image Classification Problem

Mixture of Experts with Entropic Regularization for Data Classification

Personalized polygenic risk prediction and assessment with a Mixture-of-Experts framework

Mixture-of-Experts Approach for Enhanced Drug-Target Interaction Prediction and Confidence Assessment

Mixture-of-Experts Transformers for Faithfully Deorbitalized Meta-GGA Density Functionals

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Mixture of ELM based experts with trainable gating network

Simultaneous Feature and Expert Selection within Mixture of Experts

A mixture of feature experts approach for protein-protein interaction prediction

Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping Courbariaux et al. Screening Genetic Associations for Subtyping

Scaling Molecular Representation Learning with Hierarchical Mixture-of-Experts

Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models

Online aggregation of unbounded losses using shifting experts with confidence

Beyond Deep Learning Dominance: A Scaffold-Aware Hybrid Framework for Robust Toxicity Prediction in Data-Scarce Regimes

Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

General Bayesian Predictive Synthesis

A Graph-Based Machine Learning Framework for Predicting Fuel Mixture Properties with Limited Data

Human-in-the-loop active learning for goal-oriented molecule generation