Spain

ECAI 2020

Deep learning applied to NILM : is data augmentation worth for energy disaggregation?

nilm

deep learning

data augmentation

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

ASP-based Signal Plan Configuration for Traffic Flow Optimization

Machine Learning for Gas and Oil Exploration

Point-Of-Interest Semantic Tag Completion in a Global Crowdsourced Search-and-Discovery Database

Intelligent Recommendation & Decision Technologies for Community-Driven Requirements Engineering

Cross-Border Medical Research using Multi-Layered and Distributed Knowledge

Remaining Useful Life Curve Prediction of Rolling Bearings under Defect Progression Based on Hierarchical Bayesian Regression

KnowledgeCheckeR: Intelligent Techniques for Counteracting Forgetting

FishNet: A Unified Embedding for Salmon Recognition

In this work we consider optimization problems that require to make interdependent offline and online decisions under uncertainty. We broadly refer to long-term strategic decisions as offline and to short-term operational decisions as online. For example, in Distributed Energy Management Systems we may need to define (offline) a daily production schedule for an industrial plant, and then manage (online) its power supply on a hour by hour basis. Traditionally offline and online phases are tackled in isolation, leading to some drawbacks: offline decisions are taken without regard for the capabilities of the downstream online solver; while the applicability of the best approaches for online decisions (e.g. anticipatory algorithms) is limited by the need to provide high responsiveness. Starting from a (literature-based) baseline, we define general methods for leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. All our methods have broad applicability, and provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios with both offline and online decisions and featuring continuous and discrete decisions. An extensive analysis of the experimental results shows that offline/online integration may lead to substantial benefits.

Hybrid Offline/Online Optimization Under Uncertainty

In the field of Information Retrieval, word embedding models have shown to be effective in several tasks. In this paper, we show how one of these neural embedding techniques can be adapted to the recommendation task. This adaptation only makes use of collaborative filtering information, and the results show that it is able to produce effective recommendations efficiently.

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Next from ECAI 2020

ASP-based Signal Plan Configuration for Traffic Flow Optimization

Similar lecture

MSDC: Exploiting Multi-State Power Consumption in Non-intrusive load monitoring based on A Dual-CNN Model

Stay up to date with the latest Underline news!

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