Abstract: A circuit-informed neural network (CINN) is proposed for broadening the bandwidth of substrate integrated waveguide-fed slot antennas. The proposed approach optimizes the structural ...

Researchers will often train deep neural networks on large data sets, such as articles and books, to help build a deep learning model that can recognize patterns and use ... where he learned of the ...

In the study, "A synthetic protein-level neural network in mammalian cells," published in Science, researchers showed that perceptein circuits could distinguish signal inputs with tunable decision ...

2014), To adjust the interneuron circuit in each layer (‘area’), the synaptic strengths from pyramidal-to-interneurons, , are learned to minimize the interneuron mismatch energy, . The interneurons, ...

This manuscript describes a potentially important theoretical framework to link predictive coding, error-based learning, and neuronal dynamics ... The insight into the time structure of biological ...

Researchers have developed a new technology that improves the precision and integration density of synthetic genetic circuits. Professor Jongmin Kim's research team at POSTECH developed a new ...

Researchers at IBM Quantum recently showed that simple and more accessible machine learning ... graph neural network (GNN), which they used to encode the entire structure of a quantum circuit ...

Abstract: This article introduces a novel deep learning approach, utilizing a seq2seq model, to predict far-field radiation emissions (REs) in high-density, multilayer, and complex printed circuit ...

Speaking last year about Neural Rendering, Bryan Catanzaro, an Nvidia VP of Applied Deep Learning Research explained how it was already possible to generate graphics that were entirely rendered by ...