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Found 18 papers, 7 papers with code
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other.
Engineered Ordinary Differential Equations as Classification Algorithm (EODECA): thorough characterization and testing
EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting a unique framework for classification tasks [1].
Complex Recurrent Spectral Network
This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network ($\mathbb{C}$-RSN), an innovative variant of the Recurrent Spectral Network (RSN) model.
A Bridge between Dynamical Systems and Machine Learning: Engineered Ordinary Differential Equations as Classification Algorithm (EODECA)
Recognizing the deep parallels between dense neural networks and dynamical systems, particularly in the light of non-linearities and successive transformations, this manuscript introduces the Engineered Ordinary Differential Equations as Classification Algorithms (EODECAs).
How a student becomes a teacher: learning and forgetting through Spectral methods
In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition.
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
For that, we use more than 100, 000 research papers and build up a knowledge network with more than 64, 000 concept nodes.
Noise fingerprints in quantum computers: Machine learning software tools
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer.
Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification
A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors.
Network-based link prediction of scientific concepts -- a Science4Cast competition entry
We report on a model built to predict links in a complex network of scientific concepts, in the context of the Science4Cast 2021 competition.
Learning the noise fingerprint of quantum devices
Noise sources unavoidably affect any quantum technological device.
New Trends in Quantum Machine Learning
Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications.
On the training of sparse and dense deep neural networks: less parameters, same performance
Deep neural networks can be trained in reciprocal space, by acting on the eigenvalues and eigenvectors of suitable transfer operators in direct space.
Mobility-based prediction of SARS-CoV-2 spreading
The rapid spreading of SARS-CoV-2 and its dramatic consequences, are forcing policymakers to take strict measures in order to keep the population safe.
Disordered Systems and Neural Networks
Improved bound on entropy production in a quantum annealer
For a system described by a multivariate probability density function obeying the fluctuation theorem, the average dissipation is lower-bounded by the degree of asymmetry of the marginal distributions (namely the relative entropy between the marginal and its mirror image).
Statistical Mechanics
Machine learning in spectral domain
Interestingly, spectral learning limited to the eigenvalues returns a distribution of the predicted weights which is close to that obtained when training the neural network in direct space, with no restrictions on the parameters to be tuned.
Thermodynamics of a Quantum Annealer
The D-wave processor is a partially controllable open quantum system which exchanges energy with its surrounding environment (in the form of heat) and with the external time dependent control fields (in the form of work).
Quantum Physics Statistical Mechanics
A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers
The hybrid structure of QVAE allows us to deploy current-generation quantum annealers in QCH generative models to achieve competitive performance on datasets such as MNIST.
PixelVAE++: Improved PixelVAE with Discrete Prior
Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder.
Ranked #22 on
Image Generation
on CIFAR-10
(bits/dimension metric)
