no code implementations • 17 Mar 2023 • Giorgio Giannone, Faez Ahmed
To address these issues, we propose a Generative Optimization method that integrates classic optimization like SIMP as a refining mechanism for the topology generated by a deep generative model.
no code implementations • 29 Jan 2023 • Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica
Here, we propose a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains.
no code implementations • 21 Oct 2022 • Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao
Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data.
1 code implementation • 8 Jul 2022 • Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
1 code implementation • 30 May 2022 • Giorgio Giannone, Didrik Nielsen, Ole Winther
At test time, the model is able to generate samples from previously unseen classes conditioned on as few as 5 samples from that class.
1 code implementation • 23 Oct 2021 • Giorgio Giannone, Ole Winther
In few-shot learning the model is trained on data from many sets from distributions sharing some underlying properties such as sets of characters from different alphabets or objects from different categories.
no code implementations • 7 Apr 2020 • Giorgio Giannone, Asha Anoosheh, Alessio Quaglino, Pierluca D'Oro, Marco Gallieri, Jonathan Masci
INODE is trained like a standard RNN, it learns to discriminate short event sequences and to perform event-by-event online inference.
no code implementations • 9 Dec 2019 • Giorgio Giannone, Saeed Saremi, Jonathan Masci, Christian Osendorfer
To explicitly demonstrate the effect of these higher order objects, we show that the inferred latent transformations reflect interpretable properties in the observation space.
no code implementations • 17 Dec 2018 • Giorgio Giannone, Boris Chidlovskii
We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images.