Zobrazeno 1 - 10
of 3 657
pro vyhledávání: '"Severi, P."'
Autor:
Severi, Giorgio, Boboila, Simona, Holodnak, John, Kratkiewicz, Kendra, Izmailov, Rauf, Oprea, Alina
The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks that inject backdoors in models designed for security classification task
Externí odkaz:
http://arxiv.org/abs/2407.08159
Data derived from the realm of the social sciences is often produced in digital text form, which motivates its use as a source for natural language processing methods. Researchers and practitioners have developed and relied on artificial intelligence
Externí odkaz:
http://arxiv.org/abs/2406.00393
Autor:
Chaudhari, Harsh, Severi, Giorgio, Abascal, John, Jagielski, Matthew, Choquette-Choo, Christopher A., Nasr, Milad, Nita-Rotaru, Cristina, Oprea, Alina
Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs) in chatbot applications, enabling developers to adapt and personalize the LLM output without expensive training or fine-tuning. RAG systems use an ex
Externí odkaz:
http://arxiv.org/abs/2405.20485
In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptimal compared to autoregressive generative models. This paper tackles the
Externí odkaz:
http://arxiv.org/abs/2405.17889
Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally framed as a conditional graph generation task. Diffusion models are a particularly promising modelling approach, enabling post-hoc conditioning and trading off
Externí odkaz:
http://arxiv.org/abs/2405.17656
Autor:
Severi, Leonardo, Sacchetto, Matteo, Bianco, Andrea, Rottondi, Cristina, Knapinska, Aleksandra, Lechowicz, Piotr
In this paper we present a Networked Music Performance system currently under development at Politecnico di Torino. We demonstrate its use in a distributed concert held in June 2023, which featured three musicians in Turin (Italy) and three musicians
Externí odkaz:
http://arxiv.org/abs/2404.09665
We study the quantum properties of top quark pairs in lepton colliders with unpolarised beams, including spin correlations, entanglement, and violation of Bell inequalities. We present analytical results in the SM and in the SMEFT and discuss several
Externí odkaz:
http://arxiv.org/abs/2404.08049
The recent observation of entanglement between top and anti-top quarks at the LHC opens the way to interpreting collider data with quantum information tools. In this work we investigate the relevance of quantum observables in searches of new physics.
Externí odkaz:
http://arxiv.org/abs/2401.08751
The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training. One such privacy risk is Membership Inference (MI), in which an attacker
Externí odkaz:
http://arxiv.org/abs/2310.03838
Autor:
Debenedetti, Edoardo, Severi, Giorgio, Carlini, Nicholas, Choquette-Choo, Christopher A., Jagielski, Matthew, Nasr, Milad, Wallace, Eric, Tramèr, Florian
Most current approaches for protecting privacy in machine learning (ML) assume that models exist in a vacuum. Yet, in reality, these models are part of larger systems that include components for training data filtering, output monitoring, and more. I
Externí odkaz:
http://arxiv.org/abs/2309.05610