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Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...
To achieve the goals of the Paris Agreement on climate change, governments around the world must shift to renewable energy (RE) to drastically reduce their CO2 emissions within the next 10-30 years. The development of effective strategies for RE integratio ...
Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...
In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on the experimental data from the Experimental Advanced Superconducting Tokamak ( ...
Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...
Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multi ...
The success of deep learning may be attributed in large part to remarkable growth in the size and complexity of deep neural networks. However, present learning systems raise significant efficiency concerns and privacy: (1) currently, training systems are l ...
The European fusion research activities have, over recent decades, generated a vast and varied set of data. The volume and diversity of the data that need to be catalogued and annotated make the task of organising and making the data available within a bro ...
Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Com ...
