Aimed at AI/ML applications, Renesas’ RA8P1 MCUs leverage an Arm Ethos-U55 neural processing unit (NPU) delivering 256 GOPS ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Article citations More>> Shevitski, B., Watkins, Y., Man, N. and Girard, M. (2023) Digital Signal Processing Using Deep Neural Networks: Evaluating the Effectiveness of Hybrid Autoencoder/ Transformer ...

Also available are various learning algorithms based on mathematical principles of machine learning algorithms such as Bayesian Algorithms, Instance-Based Methods, Decision Tree Algorithms, Ensemble ...

The Data Science Lab Data Dimensionality Reduction Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation ...

The Data Science Lab Data Anomaly Detection Using a Neural Autoencoder with C# Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that ...

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval.

To bridge this gap, in a new paper SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs, a research team from Google Research and Carnegie Mellon University introduces ...