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03/14/2023
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
AI-based digital twins are at the leading edge of the Industry 4.0 revol...
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10/28/2022
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
The estimation of the amount of uncertainty featured by predictive machi...
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01/11/2022
On the Design of Graph Embeddings for the Sensorless Estimation of Road Traffic Profiles
Traffic forecasting models rely on data that needs to be sensed, process...
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02/17/2021
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification
Since their inception, learning techniques under the Reservoir Computing...
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12/02/2020
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex p...
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05/11/2020
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Multitasking optimization is a recently introduced paradigm, focused on ...
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05/11/2020
On the Transferability of Knowledge among Vehicle Routing Problems by using a Cellular Evolutionary Multitasking
Multitasking optimization is a recently introduced paradigm, focused on ...
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05/08/2020
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
This work aims at unveiling the potential of Transfer Learning (TL) for ...
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04/17/2020
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
In short-term traffic forecasting, the goal is to accurately predict fut...
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03/27/2020
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
This work focuses on classification over time series data. When a time s...
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02/06/2020
