Search Results for author:
Found 25 papers, 7 papers with code
Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?
Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education.
Deep Reinforcement Learning for ESG financial portfolio management
This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation.
A survey of Generative AI Applications
Generative AI has experienced remarkable growth in recent years, leading to a wide array of applications across diverse domains.
Simulating H.P. Lovecraft horror literature with the ChatGPT large language model
In this paper, we present a novel approach to simulating H. P.
ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective
This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework.
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks
We use transfer learning to fine-tune two transformer models, BERT and ClimateBert -a recently published DistillRoBERTa-based model that has been specifically tailored for climate text classification-.
Bayesian Optimization of ESG Financial Investments
Financial experts and analysts seek to predict the variability of financial markets.
Optimizing Integrated Information with a Prior Guided Random Search Algorithm
Consequently, IIT's quantitative measure of consciousness, $\Phi$, is computed with respect to the transition probability matrix and the present state of the graph.
Do Artificial Intelligence Systems Understand?
The main thesis concerns the inevitability of semantics for any discussion about the possibility of building conscious machines, condensed into the following two tenets: "If a machine is capable of understanding (in the strong sense), then it must be capable of combining rules and intuitions"; "If semantics cannot be reduced to syntaxis, then a machine cannot understand."
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Many Objective Bayesian Optimization
We also propose a many objective Bayesian optimization algorithm that uses this metric to determine whether two objectives are redundant.
A Similarity Measure of Gaussian Process Predictive Distributions
We are really inferring that two objective functions are correlated, so one GP is enough to model both of them by performing a transformation of the prediction of the other function in case of inverse correlation.
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate.
Bayesian Optimisation
High Energy Physics - Phenomenology
Computational Physics
An Artificial Consciousness Model and its relations with Philosophy of Mind
We show in a large experiment set how an autonomous agent can benefit from having a cognitive architecture such as the one described.
Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints
MESMOC+ is also competitive with other information-based methods for constrained multi-objective Bayesian optimization, but it is significantly faster.
Comparing BERT against traditional machine learning text classification
Our purpose of this work is to add empirical evidence to support or refuse the use of BERT as a default on NLP tasks.
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This article introduces PPESMOC, Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based batch method for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints.
Fake News Detection by means of Uncertainty Weighted Causal Graphs
In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs.
Uncertainty Weighted Causal Graphs
In previous works, we have generated automatically causal graphs associated to a given concept by analyzing sets of documents and extracting and representing the found causal information in that visual way.
Multi-class Gaussian Process Classification with Noisy Inputs
The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noise.
Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
If we model as an objective function the quality of the recipe, several problems arise.
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation.
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
We show that this can lead to problems in the optimization process and describe a more principled approach to account for input variables that are categorical or integer-valued.
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
We show that this can lead to problems in the optimization process and describe a more principled approach to account for input variables that are integer-valued.
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints.
