9 citations · 42 across the 7 of their papers we have counts for
9 papers
Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
Ruixin Li, Jin Liu, Yuling Shi +1
Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right correspondences in approximat…
MAQA: A Quantum Framework for Supervised Learning
Antonio Macaluso, Matthias Klusch, Stefano Lodi +1
Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. Ho…
Quantum Splines for Non-Linear Approximations
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance. However, the main limitation is the…
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
Matteo Antonio Inajetovic, Filippo Orazi, Antonio Macaluso +2
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QS…
OptAGAN: Entropy-based finetuning on text VAE-GAN
Paolo Tirotta, Stefano Lodi
Transfer learning through large pre-trained models has changed the landscape of current applications in natural language processing (NLP). Recently Optimus, a variational autoencod…
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Ricardo Ñanculef, Francisco Mena, Antonio Macaluso +2
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient…
Quantum Ensemble for Classification
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
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 accurat…
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Emanuele Frandi, Ricardo Nanculef, Stefano Lodi +2
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. T…
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Emanuele Frandi, Ricardo Nanculef, Maria Grazia Gasparo +2
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale…