10 citations · 11 across the 2 of their papers we have counts for
5 papers
Multi-path Neural Networks for On-device Multi-domain Visual Classification
Qifei Wang, Junjie Ke, Joshua Greaves +9
Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constra…
Single-Photon Image Classification
Thomas Fischbacher, Luciano Sbaiz
Quantum computing-based machine learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at v…
Ranking architectures using meta-learning
Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz +3
Neural architecture search has recently attracted lots of research efforts as it promises to automate the manual design of neural networks. However, it requires a large amount of c…
Flexible Multi-task Networks by Learning Parameter Allocation
Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo +3
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharin…
Fast Task-Aware Architecture Inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz +3
Neural architecture search has been shown to hold great promise towards the automation of deep learning. However in spite of its potential, neural architecture search remains quite…