Natural Language Processing (Almost) from Scratch

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The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

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 ...

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 ...

Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently be ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of image ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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 ...

Natural Language Processing (Almost) from Scratch

Chat with Graph Search

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Partial discharge localization in power transformer tanks using machine learning methods

Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling

Aggregating Spatial and Photometric Context for Photometric Stereo

Glenohumeral joint force prediction with deep learning

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Fast adaptation to rule switching using neuronal surprise

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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