Search · four archives
Search · four archives
19 papers · ranked by Valyu relevance
Thomas Strypsteen, Alexander Bertrand
—In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data…
Peng Wu, Tales Imbiriba, Víctor Daniel Elvira, Pau Closas
—The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available…
Katarzyna Kusztal, Małgorzata Przybyła-Kasperek, Boris Ryabko
In distributed data environments, classification tasks are challenged by inconsistencies across independently maintained sources. These environments are inherently characterized by high informational uncertainty. Our framework addresses this challenge through a structured process designed for the reduction of entropy…
Xiaoqing Li, Jinwen Ma, Jyotismita Chaki
With the popularity of wine culture and the development of artificial intelligence (AI) technology, wine label image retrieval becomes more and more important. Taking an wine label image as an input, the goal of this task is to return the wine information that the user hopes to know, such as the main brand and…
Małgorzata Przybyła-Kasperek, Katarzyna Kusztal, Przemysław Juszczuk, Jan Kozak
'Jan Kozak'] The research concerns data collected in independent sets-more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent…
Peng Zhang, Shuyu Zhou, Peng Liu, Mengwei Li + 1 more
'Jose Manuel Molina López'] This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals…
Peng Liu, Shuyu Zhou, Peng Zhang, Mengwei Li + 1 more
For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise…
Jie Chen, Hengrui Zhang, Carolin Wahl, Wei Liu + 4 more
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected…
Debasrita Chakraborty, Ashish Ghosh
With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight…
Ruiqi Liu, Ganggang Xu, Zuofeng Shang
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification. We propose a novel distributed adaptive NN classifier for which the number of nearest neighbors is a tuning parameter stochastically…
Cesar Alfaro, Javier Gomez, Javier M. Moguerza, Javier Castillo + 2 more
'Jose I. Martinez' 'Sotiris Kotsiantis'] Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy…
Dániel Sándor, Péter Antal
In multitask federated learning, when small amounts of data are available, it can be harder to achieve proper predictive performance, especially if the clients’ tasks are different. However, task heterogeneity is common in modern Drug-Target interaction (DTI) prediction problems. As the data available for DTI tasks are…
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
It is essential to be able to combine datasets across imaging centres to represent the breadth of biological variability present in clinical populations. This, however, leads to two challenges: first, an increase in non-biological variance due to scanner differences, known as the harmonisation problem, and, second…
Murukessan Perumal, M Srinivas
Medical data is not available for public access due to privacy concerns of the patients and the stakeholders’ trust-worthiness. However, Artificial Intelligence, especially all deeplearning models, is data-hungry and fails to produce clinically relevant results without much data. Moreover, augmentation strategies are…
Till Richter, Eric Zimmermann, James Hall, Fabian J. Theis + 4 more
The vision of a “virtual cell”—a computational model that simulates biological function across modalities and scales—has become a defining goal in computational biology. While powerful unimodal foundation models exist, the lack of large-scale paired data prohibits the joint training of multimodal approaches. This…
Authors not listed
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…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…