Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Abraham Itzhak Weinberg"'
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
Human dexterity is an invaluable capability for precise manipulation of objects in complex tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects is critical for their use in the ever changing human environment
Externí odkaz:
https://doaj.org/article/1303e7c658b84fb394592eb0cd107173
Publikováno v:
Applied Sciences, Vol 14, Iss 15, p 6631 (2024)
Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment
Externí odkaz:
https://doaj.org/article/be664664bfe94e27ba9cecbb2e582d97
Autor:
Alessandro Carfì, Timothy Patten, Yingyi Kuang, Ali Hammoud, Mohamad Alameh, Elisa Maiettini, Abraham Itzhak Weinberg, Diego Faria, Fulvio Mastrogiovanni, Guillem Alenyà, Lorenzo Natale, Véronique Perdereau, Markus Vincze, Aude Billard
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop sim
Externí odkaz:
https://doaj.org/article/6ce08779ca0f46149cd5667b58fc6dd4
Autor:
Abraham Itzhak Weinberg, Mark Last
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-17 (2019)
Abstract The goal of this paper is to reduce the classification (inference) complexity of tree ensembles by choosing a single representative model out of ensemble of multiple decision-tree models. We compute the similarity between different models in
Externí odkaz:
https://doaj.org/article/b45835779e044e8b959e5969b52be442
Autor:
Abraham Itzhak Weinberg, Mark Last
Publikováno v:
Information Fusion. 89:397-404
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a149325cf2fa4d8bfba55bea44d6e9fa
https://doi.org/10.1016/j.inffus.2022.08.026
https://doi.org/10.1016/j.inffus.2022.08.026
Publikováno v:
Proceedings of the VLDB Endowment. 16:772-780
Automated machine learning (AutoML) frameworks have become important tools in the data scientist's arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b0eeb188277fd0b3fe073cb6eae03cd
https://doi.org/10.14778/3574245.3574261
https://doi.org/10.14778/3574245.3574261
Autor:
Guillem Alenyà, Abraham Itzhak Weinberg, Markus Vincze, Timothy Patten, Yingyi Kuang, Elisa Maiettini, Alessandro Carfì, Mohamad Alameh, Aude Billard, Lorenzo Natale, Ali Hammoud, Véronique Perdereau, Fulvio Mastrogiovanni, Diego R. Faria
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Frontiers in Robotics and AI
Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.714023⟩
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Frontiers in Robotics and AI
Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.714023⟩
Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop sim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a78e3ebc9d96fe646fd167a5f9a7f23
https://www.frontiersin.org/articles/10.3389/frobt.2021.714023/full
https://www.frontiersin.org/articles/10.3389/frobt.2021.714023/full
Autor:
Felipe Campelo, Abraham Itzhak Weinberg, Harry Goldingay, Michael Pritchard, John A.R. Williams, Diego R. Faria
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d1cd40c80e7206f16696404389f3380
https://publications.aston.ac.uk/id/eprint/42368/1/pdf.pdf
https://publications.aston.ac.uk/id/eprint/42368/1/pdf.pdf
Publikováno v:
RO-MAN
Reinforcement learning for multi-goal robot manipulation tasks is usually challenging, especially when sparse rewards are provided. It often requires millions of data collected before a stable strategy is learned. Recent algorithms like Hindsight Exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17c0b44abfa7239a79b03af369367a9d
https://doi.org/10.1109/ro-man47096.2020.9223473
https://doi.org/10.1109/ro-man47096.2020.9223473
Publikováno v:
Complex Engineering Systems. 2:18
This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cbb2fe9ab050d39f33f446c3c7f59c5
https://doi.org/10.20517/ces.2022.41
https://doi.org/10.20517/ces.2022.41