Deep learning
Deep learning allows computational models that are composed of multiple processing
layers to learn representations of data with multiple levels of abstraction. These methods …
layers to learn representations of data with multiple levels of abstraction. These methods …
Gradient-based learning applied to document recognition
Multilayer neural networks trained with the back-propagation algorithm constitute the best
example of a successful gradient based learning technique. Given an appropriate network …
example of a successful gradient based learning technique. Given an appropriate network …
A survey of neuromorphic computing and neural networks in hardware
CD Schuman, TE Potok, RM Patton, JD Birdwell… - ar**s from large collections of examples makes them obvious candidates for …
1.1 deep learning hardware: Past, present, and future
Y LeCun - 2019 IEEE International Solid-State Circuits …, 2019 - ieeexplore.ieee.org
Historically, progress in neural networks and deep learning research has been greatly
influenced by the available hardware and software tools. This paper identifies trends in deep …
influenced by the available hardware and software tools. This paper identifies trends in deep …
Cnp: An fpga-based processor for convolutional networks
C Farabet, C Poulet, JY Han… - … Conference on Field …, 2009 - ieeexplore.ieee.org
Convolutional networks (ConvNets) are biologically inspired hierarchical architectures that
can be trained to perform a variety of detection, recognition and segmentation tasks …
can be trained to perform a variety of detection, recognition and segmentation tasks …