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Publications associées (4)

A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks

Ralf Seifert, Yara Kayyali El Alem

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales ...

2022

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

2021

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate t ...

EPFL2021

Runtime Prediction for Scale-Out Data Analytics

Adrian Daniel Popescu

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...

EPFL2015