Publications
Publishing our work gives us the opportunity to share ideas and collaborate to advance the field of Artificial Intelligence.
TUN-AI
Assessing the effect of advertising expenditures upon sales
Personalizing Text-to-Image Generation via Aesthetic Gradients
A Machine Learning method, TUN-AI, for accurate tuna biomass estimation, benefiting the fishing industry, fishery management organizations, and conservation efforts.
Accurate sales predictions based on advertising spend benefiting various industries in strategic advertising expenditure, budget allocation, and marketing campaign planning.
The study introduces Aesthetic Gradients for personalizing diffusion models, advancing user-interactive technology and impacting fields like digital art, design, and marketing.
TUN-AI
A Machine Learning method, TUN-AI, for accurate tuna biomass estimation, benefiting the fishing industry, fishery management organizations, and conservation efforts.
Personalizing Text-to-Image Generation via Aesthetic Gradients
The study introduces Aesthetic Gradients for personalizing diffusion models, advancing user-interactive technology and impacting fields like digital art, design, and marketing.
Assessing the effect of advertising expenditures upon sales
Accurate sales predictions based on advertising spend benefiting various industries in strategic advertising expenditure, budget allocation, and marketing campaign planning.
Adversarial Machine Learning: Bayesian Perspectives
AI in drug development: a multidisciplinary perspective
How do tuna schools associate to dFADs?
A Bayesian approach to enhance defense against security threats in Machine Learning systems, applicable across cybersecurity, unsupervised learning and natural language processing.
The review explores the use of AI, particularly Bayesian approaches, in speeding up and reducing the cost of drug development across all stages.
Machine Learning to analyze tuna behavior around Fish Aggregating Devices, providing insights to enhance sustainable fishing, marine biology research and conservation policies.
Adversarial Machine Learning: Bayesian Perspectives
A Bayesian approach to enhance defense against security threats in Machine Learning systems, applicable across cybersecurity, unsupervised learning and natural language processing.
AI in drug development: a multidisciplinary perspective
The review explores the use of AI, particularly Bayesian approaches, in speeding up and reducing the cost of drug development across all stages.
How do tuna schools associate to dFADs?
Machine Learning to analyze tuna behavior around Fish Aggregating Devices, providing insights to enhance sustainable fishing, marine biology research and conservation policies.
Association Between Physical Activity And Cardiovascular Risk Factors
Augmented probability simulation methods for sequential games
Deep Neural Networks for Wind and Solar Energy Prediction
The study refutes the ‘fat but fit’ paradox, stressing the importance of weight loss in combination with physical activity to alleviate risks of cardiovascular diseases.
A scalable algorithm framework is proposed for decision-making in sequential games, relevant to areas like cybersecurity, machine learning and business competition.
Deep Learning models to improve the predictability of wind and solar energy, thereby contributing to global sustainable energy solutions and climate change mitigation.
Association Between Physical Activity And Cardiovascular Risk Factors
The study refutes the ‘fat but fit’ paradox, stressing the importance of weight loss in combination with physical activity to alleviate risks of cardiovascular diseases.
Augmented probability simulation methods for sequential games
A scalable algorithm framework is proposed for decision-making in sequential games, relevant to areas like cybersecurity, machine learning and business competition.
Deep Neural Networks for Wind and Solar Energy Prediction
Deep Learning models to improve the predictability of wind and solar energy, thereby contributing to global sustainable energy solutions and climate change mitigation.
Current Advances In Neural Networks
Inclusive dijet hadroproduction with a rapidity veto constraint
Joint association of physical activity and body mass index with cardiovascular risk
The article reviews neural networks’ progress emphasizing Bayesian approaches and highlights relevance in predictive analytics, customer behavior analysis, and risk management in business sectors.
The study explores parameters for perturbative expansion in particle collisions, helping in the understanding of particle physics. In the long term, this applies to fields like quantum computing, health technology, nuclear energy, and environmental monitoring.
The study refutes the 'fat but fit' paradox, stressing the importance of weight loss in combination with physical activity to alleviate risks of cardiovascular diseases.
Current Advances In Neural Networks
The article reviews neural networks’ progress emphasizing Bayesian approaches and highlights relevance in predictive analytics, customer behavior analysis, and risk management in business sectors.
Inclusive dijet hadroproduction with a rapidity veto constraint
The study explores parameters for perturbative expansion in particle collisions, helping in the understanding of particle physics. In the long term, this applies to fields like quantum computing, health technology, nuclear energy, and environmental monitoring.
Joint association of physical activity and body mass index with cardiovascular risk
The study refutes the 'fat but fit' paradox, stressing the importance of weight loss in combination with physical activity to alleviate risks of cardiovascular diseases.
Machine Learning Nowcasting Of PV Energy Using Satellite Data
Pomeron Physics at the LHC
Poor self‐reported sleep is associated with risk factors for cardiovascular disease
Machine learning models to improve photovoltaic (PV) energy production prediction in Spain benefiting sectors like renewable energy and finance.
The study uses Regge Theory and Pomeron exchange to enhance understanding of hadron collisions. In the long term, it influences advancements in areas like quantum computing, medical technology, defense, and energy.
The study indicates a link between poor sleep and increased cardiovascular disease risk. Emphasizing the need for sleep improvements in health strategies and policies.
Machine Learning Nowcasting Of PV Energy Using Satellite Data
Machine learning models to improve photovoltaic (PV) energy production prediction in Spain benefiting sectors like renewable energy and finance.
Pomeron Physics at the LHC
The study uses Regge Theory and Pomeron exchange to enhance understanding of hadron collisions. In the long term, it influences advancements in areas like quantum computing, medical technology, defense, and energy.
Poor self‐reported sleep is associated with risk factors for cardiovascular disease
The study indicates a link between poor sleep and increased cardiovascular disease risk. Emphasizing the need for sleep improvements in health strategies and policies.
Regression Tree Ensembles For Wind Energy And Solar Radiation Prediction
Reinforcement Learning under Threats
Variationally Inferred Sampling through a Refined Bound
The study highlights how ensemble models enhance energy forecasts, promoting better energy management and investment strategies in the renewable sector.
The study introduces Threatened Markov Decision Processes and a level-k reasoning approach. It supports decision making in adversarial reinforcement learning scenarios, relevant to cybersecurity, finance and AI.
A new framework that enhances Bayesian inference in probabilistic models enhancing the efficiency of decision-making processes in various fields including financial analysis, risk management, machine learning, and logistics.
Regression Tree Ensembles For Wind Energy And Solar Radiation Prediction
The study highlights how ensemble models enhance energy forecasts, promoting better energy management and investment strategies in the renewable sector.
Reinforcement Learning under Threats
The study introduces Threatened Markov Decision Processes and a level-k reasoning approach. It supports decision making in adversarial reinforcement learning scenarios, relevant to cybersecurity, finance and AI.
Variationally Inferred Sampling through a Refined Bound
A new framework that enhances Bayesian inference in probabilistic models enhancing the efficiency of decision-making processes in various fields including financial analysis, risk management, machine learning, and logistics.