Learning Low Resource Consumption CNN through Pruning and Quantization
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Emerging Topics in Computing"
DOI: 10.1109/tetc.2021.3050770
Abstract: Deep learning models have evolved into powerful tools that can be used for many artificial intelligence tasks. However, deploying deep neural networks into real-world applications is still challenging due to their high computational complexity and… read more here.
Keywords: channel pruning; learning low; quantization; resource consumption ... See more keywords
Reinforcement Learning With Low-Complexity Liquid State Machines
Sign Up to like & getrecommendations! Published in 2019 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2019.00883
Abstract: We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent spiking… read more here.
Keywords: state; complexity liquid; trainable parameters; learning low ... See more keywords