In a separate paper, we show how gradients can be combined with neuroevolution to improve the ability to evolve recurrent and
very deep neural networks, enabling the evolution of DNNs with over one hundred layers, a level far beyond what was previously shown possible through neuroevolution.
Not exact matches
The
deep neural network is a perception system —
very loosely inspired by animal vision, which has made huge strides in recent years.
If the training is sufficiently long (again, the training is
very computer - intensive) and the images are processed in
deep enough
networks — those with many layers of processing elements — the
neural network generalizes and can accurately recognize a new photograph as containing a feline.
When
deep learning is used on
very large data sets the
neural networks become
very smart and results are
very accurate.
ROSS uses
deep neural networks to do these
very things.
By using high - performance FPGAs, the Project Brainwave team was able to serve
Deep Neural Networks (DNNs) as hardware microservices, which reduced latency by removing the need of processing of incoming requests by the CPU, and allowed
very high throughput, because the FPGA could process requests as fast as the
network could stream them.