1 code implementation • 25 Nov 2022 • Eli Schwartz, Assaf Arbelle, Leonid Karlinsky, Sivan Harary, Florian Scheidegger, Sivan Doveh, Raja Giryes
We show that MAEDAY performs surprisingly well at this task.
no code implementations • 18 Sep 2020 • Marc Ortiz, Florian Scheidegger, Marc Casas, Cristiano Malossi, Eduard Ayguadé
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models.
no code implementations • NeurIPS 2019 • Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi
The narrow-space search of floating-point models improves the accuracy on CIFAR10 of an established IoT model from 70. 64% to 74. 87% within the same memory constraints.
no code implementations • 24 Sep 2019 • Florian Scheidegger, Luca Benini, Costas Bekas, Cristiano Malossi
We further improve the accuracy to 82. 07% by including 16-bit half types and we obtain the best accuracy of 83. 45% by extending the search with model optimized IEEE 754 reduced types.
no code implementations • 19 Sep 2019 • Giovanni Mariani, Yada Zhu, Jianbo Li, Florian Scheidegger, Roxana Istrate, Costas Bekas, A. Cristiano I. Malossi
Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty.
Computational Finance Statistical Finance
no code implementations • 17 Jan 2019 • Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
4 code implementations • 26 Mar 2018 • Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, Cristiano Malossi
The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space.
1 code implementation • 26 Mar 2018 • Florian Scheidegger, Roxana Istrate, Giovanni Mariani, Luca Benini, Costas Bekas, Cristiano Malossi
In the deep-learning community new algorithms are published at an incredible pace.