Research on the application of deep learning in low-carbon supply chain management
Sign Up to like & getrecommendations! Published in 2025 at "International Journal of Low-Carbon Technologies"
DOI: 10.1093/ijlct/ctae290
Abstract: This study proposes a deep learning-based framework to improve the efficiency and sustainability of LCSCM. Firstly, a multi-scale time series decomposition LSTM (MS-TDLSTM) model is proposed, which combines empirical mode decomposition (EMD) and attention mechanism… read more here.
Keywords: deep learning; application deep; low carbon; learning low ... See more keywords
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