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Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

Related publications (35)

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Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Chang-Hyun Park

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

OXFORD UNIV PRESS INC2021

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Network Classifiers Based On Social Learning

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

A meta-learning approach for genomic survival analysis

Arnout Jan J Devos

RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the numb ...

NATURE RESEARCH2020

Interpretable Scenicness from Sentinel-2 Imagery

Devis Tuia, Sylvain Lobry, Alex Hubertus Levering

Landscape aesthetics, or scenicness, has been identified as an important ecosystem service that contribute to human health and well-being. Currently there are no methods to inventorize landscape scenicness on a large scale. In this paper we study how to up ...

IEEE2020

Citizen Visual Search Engine: Detection and Curation of Urban Objects

Jeffrey Huang, Immanuel Chee Beng Koh

Increasingly, the ubiquity of satellite imagery has made the data analysis and machine learning of large geographical datasets one of the building blocks of visuospatial intelligence. It is the key to discover current (and predict future) cultural, social, ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Object Detection with Active Sample Harvesting

Olivier Canévet

The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between po ...

EPFL2017

Object Detection with Active Sample Harvesting

Olivier Canévet

École Polytechnique Fédérale de Lausanne2017

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Anatole von Lilienfeld, Nicholas John Browning

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large data sets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is o ...

2017