Výsledky vyhledávání - "Barsim, Karim Said"

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Variation-based Cause Effect Identification

Autor: Salem, Mohamed Amine ben, Barsim, Karim Said, Yang, Bin

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

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Multi-level Attention Model for Weakly Supervised Audio Classification

Autor: Yu, Changsong, Barsim, Karim Said, Kong, Qiuqiang, Yang, Bin

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

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Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements

Autor: Barsim, Karim Said, Mauch, Lukas, Yang, Bin

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

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On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

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

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Selective Sampling and Mixture Models in Generative Adversarial Networks

Autor: Barsim, Karim Said, Yang, Lirong, Yang, Bin

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

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Machine learning for end-use electrical energy monitoring

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

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