Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Barsim, Karim"'
Identifying the underlying reason for a failing dynamic process or otherwise anomalous observation is a fundamental challenge, yet has numerous industrial applications. Identifying the failure-causing sub-system using causal inference, one can ask th
Externí odkaz:
http://arxiv.org/abs/2406.08106
Mining genuine mechanisms underlying the complex data generation process in real-world systems is a fundamental step in promoting interpretability of, and thus trust in, data-driven models. Therefore, we propose a variation-based cause effect identif
Externí odkaz:
http://arxiv.org/abs/2211.12016
Publikováno v:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), PMLR 216:1730-1740, 2023, URL: https://proceedings.mlr.press/v216/reeb23a.html
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation offers a p
Externí odkaz:
http://arxiv.org/abs/2210.12061
In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence or absence of audio events in an audio clip. Recently, Google publishe
Externí odkaz:
http://arxiv.org/abs/1803.02353
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering. Ther
Externí odkaz:
http://arxiv.org/abs/1802.06963
Autor:
Barsim, Karim Said, Yang, Bin
Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed d
Externí odkaz:
http://arxiv.org/abs/1802.02139
In this paper, we propose a multi-generator extension to the adversarial training framework, in which the objective of each generator is to represent a unique component of a target mixture distribution. In the training phase, the generators cooperate
Externí odkaz:
http://arxiv.org/abs/1802.01568
Autor:
Barsim, Karim Said
Promoting end-users awareness of their usage and consumption of energy is one of the main measures towards achieving energy efficiency in buildings, which is one of the main targets in climate-aware energy transition programs. End-use energy disaggre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be969b069ddf55150b418527c338b6a9
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-116624
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-116624
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering. Ther
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1d118f82227b4c229e6d5c9c7c24a0d
Toward a semi-supervised non-intrusive load monitoring system for event-based energy disaggregation.
Autor:
Barsim, Karim Said, Yang, Bin
Publikováno v:
2015 IEEE Global Conference on Signal & Information Processing (GlobalSIP); 1/1/2015, p58-62, 5p
