Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Narwariya, Jyoti"'
We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple
Externí odkaz:
http://arxiv.org/abs/2203.06852
Autor:
Narwariya, Jyoti, Verma, Chetan, Malhotra, Pankaj, Vig, Lovekesh, Subramanian, Easwara, Bhat, Sanjay
In electricity markets, retailers or brokers want to maximize profits by allocating tariff profiles to end consumers. One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the ove
Externí odkaz:
http://arxiv.org/abs/2202.05517
Several applications of Internet of Things (IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based approaches for such multi-sensor or multivariate time series modeling assum
Externí odkaz:
http://arxiv.org/abs/2007.00411
Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure high operational availability. We note that most complex machinery has
Externí odkaz:
http://arxiv.org/abs/2006.16556
Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, an
Externí odkaz:
http://arxiv.org/abs/1909.07155
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization. Recurrent neur
Externí odkaz:
http://arxiv.org/abs/1907.06901
Training deep neural networks often requires careful hyper-parameter tuning and significant computational resources. In this paper, we propose ConvTimeNet (CTN): an off-the-shelf deep convolutional neural network (CNN) trained on diverse univariate t
Externí odkaz:
http://arxiv.org/abs/1904.12546
Deep neural networks have shown promising results for various clinical prediction tasks. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) requires large labeled data, significant hyper-parameter tuning effort an
Externí odkaz:
http://arxiv.org/abs/1904.00655
Publikováno v:
Journal of Healthcare Informatics Research; 20240101, Issue: Preprints p1-26, 26p
Publikováno v:
ACM International Conference Proceeding Series; 1/5/2020, p28-36, 9p