Luca Moschella
Luca Moschella
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Conference paper
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2023
2022
2021
2017
Bootstrapping Parallel Anchors for Relative Representations
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot …
Irene Cannistraci
,
Luca Moschella
,
Valentino Maiorca
,
Marco Fumero
,
Antonio Norelli
,
Emanuele Rodolà
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arXiv
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
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ICLR 2023 notable top 5%
CaSpeR: Latent Spectral Regularization for Continual Learning
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget …
Emanuele Frascaroli
,
Riccardo Benaglia
,
Matteo Boschini
,
Luca Moschella
,
Cosimo Fiorini
,
Emanuele Rodolà
,
Simone Calderara
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arXiv
Metric Based Few-Shot Graph Classification
Few-shot graph classification is a novel yet promising emerging research field that still lacks the soundness of well-established …
Donato Crisostomi
,
Simone Antonelli
,
Valentino Maiorca
,
Luca Moschella
,
Riccardo Marin
,
Emanuele Rodolà
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LoG 2022
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Aligning the visual and language spaces requires to train deep neural networks from scratch on giant multimodal datasets; CLIP trains …
Antonio Norelli
,
Marco Fumero
,
Valentino Maiorca
,
Luca Moschella
,
Emanuele Rodolà
,
Francesco Locatello
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arXiv
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their …
442 authors including
,
Andrea Santilli
,
Antonio Norelli
,
Emanuele Rodolà
,
Giambattista Parascandolo
,
Giorgio Mariani
,
Luca Moschella
,
Simone Melzi
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arXiv
Learning Spectral Unions of Partial Deformable 3D Shapes
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study …
Luca Moschella
,
Simone Melzi
,
Luca Cosmo
,
Filippo Maggioli
,
Or Litany
,
Maks Ovsjanikov
,
Leonidas Guibas
,
Emanuele Rodolà
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Computer Graphics Forum
Explanatory Learning: Beyond Empiricism in Neural Networks
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences – e.g. …
Antonio Norelli
,
Giorgio Mariani
,
Luca Moschella
,
Andrea Santilli
,
Giambattista Parascandolo
,
Simone Melzi
,
Emanuele Rodolà
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arXiv
Shape registration in the time of transformers
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed …
Giovanni Trappolini
,
Luca Cosmo
,
Luca Moschella
,
Riccardo Marin
,
Simone Melzi
,
Emanuele Rodolà
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NeurIPS 2021
Effects of Network Topology on the OpenAnswer’s Bayesian Model of Peer Assessment
The paper investigates if and how the topology of the peer-assessment network can affect the performance of the Bayesian model adopted …
Maria De Marsico
,
Luca Moschella
,
Andrea Sterbini
,
Marco Temperini
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EC-TEL 2017
Performance Variations of the Bayesian Model of Peer-Assessment Implemented in OpenAnswer Response to Modifications of the Number of Peers Assessed and of the Quality of the Class
The paper presents a study of the performance variations of the Bayesian model of peer-assessment implemented in OpenAnswer, in terms …
Maria De Marsico
,
Luca Moschella
,
Andrea Sterbini
,
Marco Temperini
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ITHET 2017
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