Titlebook: Space Fostering African Societies; Developing the Afric Annette Froehlich Book 2020 The Editor(s) (if applicable) and The Author(s), under

nepotism

Julia Selman Ayetey,Harold Ayeteyunning memory and computing resources hinder the deployment on resource-limited devices. In this paper, we propose an effective method to dynamically enhance the sparsity on channel-level. To this end, we introduce dynamic sparsity coefficient (DSC) to balance model training and sparse training as w

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Christoffel Kotzeunning memory and computing resources hinder the deployment on resource-limited devices. In this paper, we propose an effective method to dynamically enhance the sparsity on channel-level. To this end, we introduce dynamic sparsity coefficient (DSC) to balance model training and sparse training as w

Indecisive

Nicolas RingasNs may generate redundant information in the message passing phase. In order to solve this problem, we propose a novel graph convolution named Push-and-Pull Convolution (PPC), which follows the message passing framework. On the one hand, for each star-shaped subgraph, PPC uses a node pair based mess

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Khaled Khanbari,Sylvie Leroy,Ahmad Adris,Sami Moheb-Al-Deen,Waheed Al-SarariNs may generate redundant information in the message passing phase. In order to solve this problem, we propose a novel graph convolution named Push-and-Pull Convolution (PPC), which follows the message passing framework. On the one hand, for each star-shaped subgraph, PPC uses a node pair based mess

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LAPSE

Annette Froehlichtimation, Edge Estimation, etc. With advanced deep learning, many dense prediction tasks have been greatly improved. Multi-task learning is one of the top research lines to boost task performance further. Properly designed multi-task model architectures have better performance and minor memory usage

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Temidayo Oniosun,Ndéye Marie Aida Ndieguene,Mwenya Mwamba,Sharon Kendi Amugongo,Oluwafunmilayo Oluwa. Nevertheless, deploying large DNN models on resource-constrained edge devices is still challenging due to limitations in computation, power, and application-specific privacy requirements. Existing model partitioning methods, which deploy a partial DNN on an edge device while processing the remaini

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Nicolas Ringas,James Wilson,Asim Raza,Bafowethu Setheli,Barbara Amelia King,Jahanzaib Hussain,Luke Cs a feature extractor and replace the classification head with a decoder to generate segmented outputs. The advantage of this strategy is the ability to obtain a ready-made backbone with additional knowledge. However, there are several disadvantages, such as a lack of architectural knowledge, a sign