4 citations · 6 across the 11 of their papers we have counts for
13 papers
Geodesic-SVDD Latent Mixup for One-Shot Distributed Tabular Learning
Andrea Moleri, Christian Internò, Markus Olhofer +2
Distributed Learning on structured data is a key and widely adopted approach in privacy-sensitive sectors, ranging from financial fraud prevention to federated healthcare diagnosti…
Is a Linear Probe Evidence of a Linear Representation?
Nadir Kutluozen, Christian Internò, David Klindt
The linear representation hypothesis holds that pretrained vision models encode generative factors as linearly decodable directions in feature space, and comes with a standard meth…
Procedural Core: A Compact Recurrent Initialization for Vision Transformers
Zachary Shinnick, Christian Internò, Hemanth Saratchandran +2
Transformers are typically trained from random initialization, requiring all their capabilities to emerge from large-scale optimization. Recent work showed that a small amount of a…
MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space
Markus Karmann, Shile Li, Christian Internò +10
Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction. However, standard JEPA train…
GEOPHYS: The Geometry of Physical Plausibility
Christian Internò, Alexander Pondaven, Habon Issa +8
While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow and expensive. They either r…
FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
Andrea Moleri, Christian Internò, Ali Raza +4
Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggrega…
The Observer Effect in World Models: Invasive Adaptation Corrupts Latent Physics
Christian Internò, Jumpei Yamaguchi, Loren Amdahl-Culleton +3
Determining whether neural models internalize physical laws as world models, rather than exploiting statistical shortcuts, remains challenging, especially under out-of-distribution…
The Narcissus Hypothesis: Descending to the Rung of Illusion
Riccardo Cadei, Christian Internò
Modern foundational models increasingly reflect not just world knowledge, but patterns of human preference embedded in their training data. We hypothesize that recursive alignment-…
AI-Generated Video Detection via Perceptual Straightening
Christian Internò, Robert Geirhos, Markus Olhofer +3
The rapid advancement of generative AI enables highly realistic synthetic videos, posing significant challenges for content authentication and raising urgent concerns about misuse.…
Federated Loss Exploration for Improved Convergence on Non-IID Data
Christian Internò, Markus Olhofer, Yaochu Jin +1
Federated learning (FL) has emerged as a groundbreaking paradigm in machine learning (ML), offering privacy-preserving collaborative model training across diverse datasets. Despite…
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
Christian Internò, Andrea Castellani, Sebastian Schmitt +2
Industrial Non-Intrusive Load Monitoring (NILM) is limited by the scarcity of high-quality datasets and the complex variability of industrial energy consumption patterns. To addres…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data
Christian Internò, Eloisa Ambrosini
In recent years, there is strong emphasis on mining medical data using machine learning techniques. A common problem is to obtain a noiseless set of textual documents, with a relev…