Stefano Lodi
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View article: Double Wishbone Suspension: A Computational Framework for Parametric 3D Kinematic Modeling and Simulation Using Mathematica
Double Wishbone Suspension: A Computational Framework for Parametric 3D Kinematic Modeling and Simulation Using Mathematica Open
The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties, which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D mo…
View article: Double Wishbone Suspension: A Computational Framework for Parametric 3D Kinematic Modeling and Simulation Using Mathematica
Double Wishbone Suspension: A Computational Framework for Parametric 3D Kinematic Modeling and Simulation Using Mathematica Open
The double wishbone suspension (DWS) system is widely used in automotive engineering because of its favorable kinematic properties which affect vehicle dynamics, handling, and ride comfort; hence, it is important to have an accurate 3D mod…
View article: Multi-Objective Optimization of Independent Automotive Suspension by AI and Quantum Approaches: A Systematic Review
Multi-Objective Optimization of Independent Automotive Suspension by AI and Quantum Approaches: A Systematic Review Open
The optimization of independent automotive suspension systems, which is one of the main pillars of the vehicle performance and comfort, is currently going through a revolutionary change due to the development of artificial intelligence and…
View article: A clustering aggregation algorithm on neutral-atoms and annealing quantum processors
A clustering aggregation algorithm on neutral-atoms and annealing quantum processors Open
This work presents a hybrid quantum-classical algorithm to perform clustering aggregation, designed for neutral-atoms quantum computers and quantum annealers. Clustering aggregation is a technique that mitigates the weaknesses of clusterin…
View article: An efficient quantum algorithm for ensemble classification using bagging
An efficient quantum algorithm for ensemble classification using bagging Open
Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time r…
View article: Hybrid Quantum Technologies for Quantum Support Vector Machines
Hybrid Quantum Technologies for Quantum Support Vector Machines Open
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm …
View article: Enabling Non-Linear Quantum Operations through Variational Quantum Splines
Enabling Non-Linear Quantum Operations through Variational Quantum Splines Open
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QSplines) have recently been proposed to approximate quantum …
View article: Quantum Ensemble for Classification
Quantum Ensemble for Classification Open
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and have a lower variance than the individual classifiers…
View article: OptAGAN: Entropy-based Finetuning on Text VAE-GAN
OptAGAN: Entropy-based Finetuning on Text VAE-GAN Open
Transfer learning through large pre-trained models has changed the landscape of current applications in natural language processing (NLP). Recently Optimus, a variational autoencoder (VAE) which combines two pre-trained models, BERT and GP…
View article: Quantum Ensemble for Classification
Quantum Ensemble for Classification Open
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that m…
View article: Quantum splines for non-linear approximations
Quantum splines for non-linear approximations Open
Quantum Computing offers a new paradigm for efficient computing and many AI\napplications could benefit from its potential boost in performance. However,\nthe main limitation is the constraint to linear operations that hampers the\nreprese…
View article: Parallel Primitives for Vendor-Agnostic Implementation of Big Data Mining Algorithms
Parallel Primitives for Vendor-Agnostic Implementation of Big Data Mining Algorithms Open
In the age of Big Data, scalable algorithm implementations as well as powerful computational resources are required. For data mining and data analytics the support of big data platforms is becoming increasingly important, since they provid…
View article: Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee Open
Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning. The ability to work with cheap projection-free iterations an…
View article: Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a\n Convergence Guarantee
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a\n Convergence Guarantee Open
Frank-Wolfe (FW) algorithms have been often proposed over the last few years\nas efficient solvers for a variety of optimization problems arising in the\nfield of Machine Learning. The ability to work with cheap projection-free\niterations…