Placing too much emphasis on a specific interpretation of dimensionality may lead neuroscience down the wrong path.

Brenton said the company is changing its guidance approach: “we have decided to withdraw our full year financial guidance at this time,” citing “the timing and scope of potential monetization actions ...

The classy vocal quintet has long been underrated. Fortunately, they got their flowers with Questlove's Oscar-winning doc, Summer of Soul. By Paul Grein The death on Tuesday Feb. 3 of LaMonte McLemore ...

Abstract: Faced with high-dimensional expensive optimization problems (HEOPs), existing high-dimensional expensive optimization algorithms (HEOAs) struggle to locate promising areas quickly due to a ...

An autoencoder trained on representative normal data learns to reconstruct that distribution. Inputs that differ materially from its training examples can produce ...

<!— slug: autoencoders-the-neural-networks-that-teach-themselves-compression —> <!— excerpt: Learn how autoencoders compress data through neural bottlenecks. Covers denoising, sparse, and variational ...

The challenge of classifying data with numerous features currently limits the scope of many algorithms, hindering their application to complex problems. Patrick Odagiu, Vasilis Belis, and Lennart ...

This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...

CAD-DR is a deep learning-based system for dimensionality reduction of 3D CAD models using a 3D convolutional autoencoder. The system supports full STL to voxel transformation, encoding, ...

A navigating axon faces complex choices when selecting postsynaptic partners in a three-dimensional (3D) space. In this work, we discovered a principle that can establish the 3D glomerular map of the ...