21 papers · ranked by Valyu relevance
Hamed Jalali, Gjergji Kasneci, Robertas Alzbutas, Mark Girolami + 1 more
'Hussein Rappel'] By distributing the training process, local approximation reduces the cost of the standard Gaussian process. An ensemble method aggregates predictions from local Gaussian experts, each trained on different data partitions, under the assumption of perfect diversity among them. While this assumption…
Yuanteng Chen, Yuantian Shao, Peisong Wang, Jian Cheng
Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs. However, it is hindered by two critical challenges: (1) substantial GPU memory consumption to load all experts; (2) low activated parameters cannot be equivalently translated into inference acceleration effects. In this work, we propose…
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…
Hamed Jalali, Gjergji Kasneci
—By distributing the training process, local approximation reduces the cost of the standard Gaussian Process. An ensemble technique combines local predictions from Gaussian experts trained on different partitions of the data. Ensemble methods aggregate models' predictions by assuming a perfect diversity of local…
Shengling Qin, Hai Wu, Hongyang Du, Kaibin Huang
—The emergence of distributed Mixture-of-Experts (DMoE) systems, which deploy expert models at edge nodes, offers a pathway to achieving connected intelligence in sixthgeneration (6G) mobile networks and edge artificial intelligence (AI). However, current DMoE systems lack an effective expert selection algorithm to…
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…
Jaroslaw Kornowicz, Kirsten Thommes
Methods on User Preferences and Reliance Authors: ['Jaroslaw Kornowicz' 'Kirsten Thommes'] The integration of users and experts in machine learning is a widely studied topic in artificial intelligence literature. Similarly, human-computer interaction research extensively explores the factors that influence the…
Authors not listed
Selection of efficient multi-step synthesis routes is a fundamental challenge in organic synthesis. Comparing different routes involves numerous parameters, economic considerations, and integration of nuanced chemical knowledge. While computer-aided synthesis planning (CASP) tools can generate synthetic routes…
Kai Heinrich, Christian Janiesch, Oliver Krancher, Philip Stahmann + 3 more
'Jonas Wanner' 'Patrick Zschech' 'Alessio Luschi'] Decision support systems (DSS) integrating artificial intelligence (AI) hold the potential to significantly enhance organizational decision-making performance and speed in areas such as prognostics in machine maintenance. A key issue for organizations aiming to…
Sara Rye, Emel Aktas, Paul B. Tchounwou
In this paper, we validate PREDIS, a decision support system for disaster management using serious games to collect experts’ judgments on its performance. PREDIS is a model for DISaster response supplier selection (PREDIS). It has a PREDictive component (PRED) for predicting the disaster human impact and an estimation…
Li Wang, Kunhui Ye, Yu Liu, Wenjing Wang
Experts play a crucial role in underpinning decision-making in most management situations. While recent studies have disclosed the impacts of individuals’ inherent cognition and the external environment on expert performance, these two-dimensional mechanisms remain poorly understood. In this study, we identified 14…
Ingeborg Gullikstad Hem, Maria Lie Selle, Gregor Gorjanc, Geir-Arne Fuglstad + 1 more
A major challenge with modelling non-additive genetic variation is that it is hard to separate it from additive and environmental variation. In this paper, we describe how to alleviate this issue and improve genomic modelling of additive and non-additive variation, by leveraging expert knowledge available about the…
Michael Halter, Steven Lund, Adele Peskin, Ya-Shian Li-Baboud + 7 more
The visual inspection of pluripotent stem cell colonies by microscopy is widely used as a primary method to assess the quality of the preparations and degree of pluripotency. The lack of ground truth and the possible inconsistency of evaluations from multiple experts within and between stem cell laboratories are…
Lucas Böttcher, Ronald Klingebiel
Budgetary constraints force organizations to pursue only a subset of possible innovation projects. Identifying which subset is most promising is an error-prone exercise, and involving multiple decision makers may be prudent. This raises the question of how to most effectively aggregate their collective nous. Our model…
Tehmina Hafeez, Sanay Muhammad Umar Saeed, Aamir Arsalan, Syed Muhammad Anwar + 2 more
Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help provide an optimal gaming experience level by improving video game design. To this end, we present a framework for classifying the game…
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…
Lyle E. Bourne Jr., James A. Kole, Alice F. Healy
We all know an expert when we see one. Normally people will quickly recognize the difference between expertise and normal or ordinary performance in any domain. Expertise, itself, is a descriptive term. To describe is to add detail in the specific case to a more general definition. A description of expertise requires…
Mai P. Trinh
Although experts are valuable assets to organizations, they suffer from the curse of knowledge and cognitive entrenchment, which prevents them from being able to adapt to changing situational demands. In this study, I propose that experts’ performance goal orientation resulting from pressures to perform contributes to…
Dilan Pathirana, Frank T. Bergmann, Domagoj Doresic, Polina Lakrisenko + 8 more
A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the…
Authors not listed
Machine learning (ML) has emerged as a transformative approach to accelerate material discovery. A critical challenge in building predictive ML models is the selection of representative and informative training datasets from large databases. In this study, we present a framework that integrates inducing points and…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…