Learning Aerial Image Segmentation from Online Maps

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A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Learning Aerial Image Segmentation from Online Maps

Chat with Graph Search

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Robust machine learning for neuroscientific inference

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Partial discharge localization in power transformer tanks using machine learning methods

Robust NAS under adversarial training: benchmark, theory, and beyond

Predicting the long-term collective behaviour of fish pairs with deep learning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Meta-learning to address diverse Earth observation problems across resolutions

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models