Search Results for author:
Found 14 papers, 4 papers with code
Fast Analysis of the OpenAI O1-Preview Model in Solving Random K-SAT Problem: Does the LLM Solve the Problem Itself or Call an External SAT Solver?
In this manuscript, I present an analysis on the performance of OpenAI O1-preview model in solving random K-SAT instances for K$\in {2, 3, 4}$ as a function of $\alpha=M/N$ where $M$ is the number of clauses and $N$ is the number of variables of the satisfiable problem.
Learning in Wilson-Cowan model for metapopulation
The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions.
Ranked #1 on
Image Classification
on Flowers (Tensorflow)
Automatic Input Feature Relevance via Spectral Neural Networks
Working with high-dimensional data is a common practice, in the field of machine learning.
The Garden of Forking Paths: Observing Dynamic Parameters Distribution in Large Language Models
A substantial gap persists in understanding the reasons behind the exceptional performance of the Transformer architecture in NLP.
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.
Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)
A novel approach for supervised classification is presented which sits at the intersection of machine learning and dynamical systems theory.
Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems
Is Stochastic Gradient Descent (SGD) substantially different from Metropolis Monte Carlo dynamics?
Phase transitions in the mini-batch size for sparse and dense two-layer neural networks
This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks.
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
In particular, for 90% of random 4-SAT instances we find solutions that are inaccessible for the best specialized deterministic algorithm known as Survey Propagation (SP) with an order of magnitude improvement in the quality of solutions for the hardest 10% instances.
Learning from Survey Propagation: a Neural Network for MAX-E-$3$-SAT
This paper presents a new algorithm for computing approximate solutions in ${\Theta(N})$ for the Maximum Exact 3-Satisfiability (MAX-E-$3$-SAT) problem by using deep learning methodology.
Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes
The main idea is to construct a deep network which is trained from the samples of discrete stochastic differential equations underlying Kolmogorov's equation.
The backtracking survey propagation algorithm for solving random K-SAT problems
Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances.
