Department of Computer Science

Sapienza, University of Rome

About us

Welcome to the GLADIA research lab! Based in the beautiful city of Rome, we are a dynamic team of computer scientists, physicists, engineers, and mathematicians who are passionate about AI and love to push the boundaries of what is possible, seeking innovative solutions to real-world problems – or sometimes, making up new ones. We work with images, molecules, graphs, 3D shapes, music, language, games, medical data, and quantum particles. We are always excited to collaborate with other disciplines, bring our expertise to the table and have fun with science!

Google Research Award "FairGeometry"
ERC StG 2018 "SPECGEO"

News

We have 3/3 papers accepted at NeurIPS 2023!

Jul 20, 2023

We are co-organizing the 1st Edition of the UniReps Workshop on Unifying Representations at NeurIPS 2023!

Jul 17, 2023

Our paper “Accelerating Transformer Inference for Translation via Parallel Decoding” was accepted to ACL 2023 main conference!

May 18, 2023

We release Camoscio: An Italian instruction-tuned LLaMA! Checkout the details here (Italian) or here (English).

Apr 3, 2023

Our paper “Relative representations enable zero-shot latent space communication” was accepted to ICLR 2023 as an Oral (Notable Top 5%) presentation!

Feb 6, 2023

Featured Publications

Find the complete list in publications.

Accelerating Transformer Inference for Translation via Parallel Decoding

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network …

Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodolà

Latent Autoregressive Source Separation

Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task …

Emilian Postolache, Giorgio Mariani, Michele Mancusi, Andrea Santilli, Luca Cosmo, Emanuele Rodolà

Latent Autoregressive Source Separation

Relative representations enable zero-shot latent space communication

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. …

Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà

Relative representations enable zero-shot latent space communication

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Nature Methods 2020

Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level …

P. Gainza, F. Sverrisson, F. Monti, Emanuele Rodolà, Davide Boscaini, M. M. Bronstein, B. Correia

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

LIMP: Learning Latent Shape Representations with Metric Preservation Priors

In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D …

Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà

Isospectralization, or how to hear shape, style, and correspondence

The question whether one can recover the shape of a geometric object from its Laplacian spectrum (‘hear the shape of the …

Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà

Isospectralization, or how to hear shape, style, and correspondence

Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model …

O. Litany, T. Remez, Emanuele Rodolà, A. M. Bronstein, M. M. Bronstein

Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

Projects

Open Source

An Italian instruction-tuned LLaMA

Andrea Santilli

Camoscio

Collection of manim components, layouts and slide templates aimed to ease the development of animations, with particular focus on live presentations.

Powermanim

Newton's Fractals on Surfaces

This repository contains a Unity3D project implementing a demo for the method presented in Newton’s Fractals on Surfaces via Bicomplex Algebra, published at ACM SIGGRAPH 2022 in the Poster program.

Filippo Maggioli

Newton's Fractals on Surfaces

Any Order FEM Laplacian

Finite Element Method (FEM) discretization of the Laplace-Beltrami operator on triangular meshes, parametric on the polynomial’s order of the hat functions.

Filippo Maggioli

Any Order FEM Laplacian

Popular template to bootstrap the scaffolding for your PyTorch project with PyTorch Lightning, Hydra, DVC, Weights and Biases, and Streamlit.

Valentino Maiorca, Luca Moschella

NN Template

Contact

rodola@di.uniroma1.it
Via Salaria, 113, Rome, RM 00198
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