Zobrazeno 1 - 10
of 22 106
pro vyhledávání: '"Selva A."'
Autor:
Annamalai, Meenatchi Sundaram Muthu Selva, Balle, Borja, De Cristofaro, Emiliano, Hayes, Jamie
Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular method for training machine learning models with formal Differential Privacy (DP) guarantees. As DP-SGD processes the training data in batches, it uses Poisson sub-sampling to s
Externí odkaz:
http://arxiv.org/abs/2411.10614
This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal struct
Externí odkaz:
http://arxiv.org/abs/2411.02331
We present a simple toy model of cosmic acceleration driven purely by a self-interacting scalar field embedded in theory of grand unification. The scalar self-interaction is Higgs-like and provokes a spontaneous symmetry breaking. The coefficient of
Externí odkaz:
http://arxiv.org/abs/2409.15962
Autor:
Nicoletti, Jacopo, Puppulin, Leonardo, Routurier, Julie, Frroku, Saimir, Loudhaief, Nouha, Crestini, Claudia, Perosa, Alvise, Selva, Maurizio, Gigli, Matteo, De Fazio, Domenico, Salvatore, Giovanni Antonio
Piezoelectricity, the generation of electric charge in response to mechanical stress, is a key property in both natural and synthetic materials. This study significantly boosts the piezoelectric response of chitosan, a biodegradable biopolymer, by in
Externí odkaz:
http://arxiv.org/abs/2407.18585
Publikováno v:
Published in the Proceedings of the 17th ACM Workshop on Artificial Intelligence and Security (AISec 2024), please cite accordingly
Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular iterative algorithm used to train machine learning models while formally guaranteeing the privacy of users. However, the privacy analysis of DP-SGD makes the unrealistic assumpt
Externí odkaz:
http://arxiv.org/abs/2407.06496
Synthetic data created by differentially private (DP) generative models is increasingly used in real-world settings. In this context, PATE-GAN has emerged as a popular algorithm, combining Generative Adversarial Networks (GANs) with the private train
Externí odkaz:
http://arxiv.org/abs/2406.13985
This paper presents an auditing procedure for the Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm in the black-box threat model that is substantially tighter than prior work. The main intuition is to craft worst-case initial mod
Externí odkaz:
http://arxiv.org/abs/2405.14106
Differentially private synthetic data generation (DP-SDG) algorithms are used to release datasets that are structurally and statistically similar to sensitive data while providing formal bounds on the information they leak. However, bugs in algorithm
Externí odkaz:
http://arxiv.org/abs/2405.10994
Autor:
S, Selva Kumar, Khan, Afifah Khan Mohammed Ajmal, Banday, Imadh Ajaz, Gada, Manikantha, Shanbhag, Vibha Venkatesh
This research introduces an innovative AI-driven precision agriculture system, leveraging YOLOv8 for disease identification and Retrieval Augmented Generation (RAG) for context-aware diagnosis. Focused on addressing the challenges of diseases affecti
Externí odkaz:
http://arxiv.org/abs/2405.01310
Exploring Li-ion Transport Properties of Li$_3$TiCl$_6$: A Machine Learning Molecular Dynamics Study
Publikováno v:
Journal of the Electrochemical Society 171 (2024) 050544
We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li$_3$TiCl$_6$ cathode at six different temperatures, ranging from 25$^\mathrm{o
Externí odkaz:
http://arxiv.org/abs/2403.01077