Logic Tensor Network (LTN) is a Neural-Symbolic (NeSy) framework which supports learning of neural networks using the satisfaction of a first-order logic knowledge base as an objective. In other words ...

This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build ...

During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) ...

Deep learning (DL) systems have been widely adopted in many industrial and business applications, dramatically improving human productivity, and enabling new industries. However, deep learning has a ...

However, implementing trained mathematical transformations by designing hardware for strict, operation-by-operation mathematical isomorphism is not the only way to perform efficient machine learning.

Public databases are an important resource for machine learning research, but their growing availability sometimes leads to “off-label” usage, where data published for one task are used for another.

Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the ...

Dr. James McCaffrey of Microsoft Research continues his examination of creating a PyTorch neural network binary classifier through six steps, here addressing step No. 4: training the network. The goal ...

Robotic exoskeletons are developed with the aim of enhancing convenience and physical possibilities in daily life. However, at present, these devices lack sufficient synchronization with human ...