Search Results for author: Luciano Sbaiz

Found 5 papers, 0 papers with code

Multi-path Neural Networks for On-device Multi-domain Visual Classification

no code implementations10 Oct 2020 Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew Howard, Feng Yang, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar

This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.

Classification General Classification +2

Single-Photon Image Classification

no code implementations ICLR 2021 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 very low temperature.

Classification General Classification +1

Ranking architectures using meta-learning

no code implementations26 Nov 2019 Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent

However, it requires a large amount of computing resources and in order to alleviate this, a performance prediction network has been recently proposed that enables efficient architecture search by forecasting the performance of candidate architectures, instead of relying on actual model training.

Meta-Learning Neural Architecture Search

Flexible Multi-task Networks by Learning Parameter Allocation

no code implementations10 Oct 2019 Krzysztof Maziarz, Efi Kokiopoulou, Andrea Gesmundo, Luciano Sbaiz, Gabor Bartok, Jesse Berent

The binary allocation variables are learned jointly with the model parameters by standard back-propagation thanks to the Gumbel-Softmax reparametrization method.

Multi-Task Learning

Fast Task-Aware Architecture Inference

no code implementations15 Feb 2019 Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent

At the core of our framework lies a deep value network that can predict the performance of input architectures on a task by utilizing task meta-features and the previous model training experiments performed on related tasks.

Neural Architecture Search

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