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Found 5 papers, 1 papers with code
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.
Taxonomy and evolution predicting using deep learning in images
Molecular and morphological characters, as important parts of biological taxonomy, are contradictory but need to be integrated.
Mushroom image recognition and distance generation based on attention-mechanism model and genetic information
In this paper, we propose a new model based on attention-mechanism, MushroomNet, which applies the lightweight network MobileNetV3 as the backbone model, combined with the attention structure proposed by us, and has achieved excellent performance in the mushroom recognition task.
Automated airway segmentation by learning graphical structure
In our model, with the neighbourhoods of airway taken into account, the graph structure is incorporated and the segmentation of airways are improved compared with the traditional CNN methods.
Dual-module Inference for Efficient Recurrent Neural Networks
Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs.
