Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Ruocco, Massimiliano"'
Autor:
Kristoffersen, Simen, Nordby, Peter Skaar, Malacarne, Sara, Ruocco, Massimiliano, Ortiz, Pablo
We introduce SiamTST, a novel representation learning framework for multivariate time series. SiamTST integrates a Siamese network with attention, channel-independent patching, and normalization techniques to achieve superior performance. Evaluated o
Externí odkaz:
http://arxiv.org/abs/2407.02258
Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios an
Externí odkaz:
http://arxiv.org/abs/2406.04890
Urbanization has underscored the importance of understanding the pedestrian wind environment in urban and architectural design contexts. Pedestrian Wind Comfort (PWC) focuses on the effects of wind on the safety and comfort of pedestrians and cyclist
Externí odkaz:
http://arxiv.org/abs/2311.07985
A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for
Externí odkaz:
http://arxiv.org/abs/2310.20476
Autor:
Sørbø, Sondre, Ruocco, Massimiliano
The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of met
Externí odkaz:
http://arxiv.org/abs/2303.01272
Time series forecasting is an important problem, with many real world applications. Ensembles of deep neural networks have recently achieved impressive forecasting accuracy, but such large ensembles are impractical in many real world settings. Transf
Externí odkaz:
http://arxiv.org/abs/2208.14236
Autor:
Hoeiness, Henrik, Gjerde, Kristoffer, Oggiano, Luca, Giljarhus, Knut Erik Teigen, Ruocco, Massimiliano
Approximating wind flows using computational fluid dynamics (CFD) methods can be time-consuming. Creating a tool for interactively designing prototypes while observing the wind flow change requires simpler models to simulate faster. Instead of runnin
Externí odkaz:
http://arxiv.org/abs/2112.08447
One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are continuously made av
Externí odkaz:
http://arxiv.org/abs/1909.01757
Recently, Convolutional Neural Networks (CNNs) have shown unprecedented success in the field of computer vision, especially on challenging image classification tasks by relying on a universal approach, i.e., training a deep model on a massive dataset
Externí odkaz:
http://arxiv.org/abs/1905.09247
In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several addi
Externí odkaz:
http://arxiv.org/abs/1812.01276