5 citations · 13 across the 14 of their papers we have counts for
14 papers
AsyncPatch Diffusion: spatially-flexible image generation
Samuele Papa, Valentin De Bortoli, Guillaume Couairon +3
Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Di…
From MLP to NeoMLP: Leveraging Self-Attention for Neural Fields
Miltiadis Kofinas, Samuele Papa, Efstratios Gavves
Neural fields (NeFs) have recently emerged as a state-of-the-art method for encoding spatio-temporal signals of various modalities. Despite the success of NeFs in reconstructing in…
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination
Leonardo Barcellona, Andrii Zadaianchuk, Davide Allegro +3
A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot direc…
NeuralDEM -- Real-time Simulation of Industrial Particulate Flows
Benedikt Alkin, Tobias Kronlachner, Samuele Papa +3
Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial…
NARAIM: Native Aspect Ratio Autoregressive Image Models
Daniel Gallo Fernández, Robert van der Klis, Răzvan-Andrei Matişan +4
While vision transformers are able to solve a wide variety of computer vision tasks, no pre-training method has yet demonstrated the same scaling laws as observed in language model…
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Amir Mohammad Karimi Mamaghan, Samuele Papa, Karl Henrik Johansson +2
Object-centric (OC) representations, which model visual scenes as compositions of discrete objects, have the potential to be used in various downstream tasks to achieve systematic…
Space-Time Continuous PDE Forecasting using Equivariant Neural Fields
David M. Knigge, David R. Wessels, Riccardo Valperga +4
Recently, Conditional Neural Fields (NeFs) have emerged as a powerful modelling paradigm for PDEs, by learning solutions as flows in the latent space of the Conditional NeF. Althou…
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
David R Wessels, David M Knigge, Samuele Papa +4
Conditional Neural Fields (CNFs) are increasingly being leveraged as continuous signal representations, by associating each data-sample with a latent variable that conditions a sha…
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
Samuele Papa, Riccardo Valperga, David Knigge +4
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works h…
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Samuele Papa, David M. Knigge, Riccardo Valperga +4
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex…
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
Gabriel Bénédict, Olivier Jeunen, Samuele Papa +3
In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation. Unlike image data which contain spatial correlations, a user-item interaction matri…
Inductive Biases for Object-Centric Representations in the Presence of Complex Textures
Samuele Papa, Ole Winther, Andrea Dittadi
Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. In this paper, we systematic…
Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi, Samuele Papa, Michele De Vita +3
The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed represe…