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Found 86 papers, 27 papers with code
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child
World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making.
The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways
This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field.
Can transformative AI shape a new age for our civilization?: Navigating between speculation and reality
Artificial Intelligence is widely regarded as a transformative force with the potential to redefine numerous sectors of human civilization.
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark.
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation Disagreement
Content moderation typically combines the efforts of human moderators and machine learning models.
A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas
Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks.
Words as Beacons: Guiding RL Agents with High-Level Language Prompts
Sparse reward environments in reinforcement learning (RL) pose significant challenges for exploration, often leading to inefficient or incomplete learning processes.
Fostering Intrinsic Motivation in Reinforcement Learning with Pretrained Foundation Models
Exploration remains a significant challenge in reinforcement learning, especially in environments where extrinsic rewards are sparse or non-existent.
A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems
This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency.
A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks
This allows the local fitness function to be defined as the ratio between the activation of positive neurons and the overall layer activity, resulting in a symmetric loss landscape during the training phase.
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
However, this algorithm still faces weaknesses that negatively affect the model accuracy and training stability, primarily due to a gradient imbalance between positive and negative samples.
On the Robustness of Fully-Spiking Neural Networks in Open-World Scenarios using Forward-Only Learning Algorithms
In the last decade, Artificial Intelligence (AI) models have rapidly integrated into production pipelines propelled by their excellent modeling performance.
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data are partially labeled.
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation.
Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing
To verify this result, we compare the training behavior of FFA in analog networks with its Hebbian adaptation in spiking neural networks.
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task.
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence
This work analyses the value of data morphology strategies in generating counterfactual explanations.
When AI Eats Itself: On the Caveats of AI Autophagy
Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music.
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning
The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space.
Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage.
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
Besides performance, efficiency is a key design driver of technologies supporting vehicular perception.
Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
In this work, we investigate the means of using curiosity on replay buffers to improve offline multi-task continual reinforcement learning when tasks, which are defined by the non-stationarity in the environment, are non labeled and not evenly exposed to the learner in time.
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informative feedback signals.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task.
ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification
In the era of sustainable smart agriculture, a massive amount of agricultural news text is being posted on the Internet, in which massive agricultural knowledge has been accumulated.
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective.
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments.
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
AI-based digital twins are at the leading edge of the Industry 4. 0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis.
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness
MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models.
Deep Learning for Brain Age Estimation: A Systematic Review
In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Unfortunately, in a broad range of problems the design of a good reward function is not trivial, so in such cases sparse reward signals are instead adopted.
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years.
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
Specifically, this work presents a novel OoD detector that can identify whether test examples input to a Spiking Neural Network belong to the distribution of the data over which it was trained.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques
The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights).
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Data quality is the key factor for the development of trustworthy AI in healthcare.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness.
Deep learning for understanding multilabel imbalanced Chest X-ray datasets
To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques.
Large-Kernel Attention for 3D Medical Image Segmentation
The performance improvement due to the proposed LK attention module was also statistically validated.
Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation
This work validates the applicability of CycleGANs on style transfer from an initial sketch to a final render in 2D that represents a 3D design, a step that is paramount in every product design process.
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools.
An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments
In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable.
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i. e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?
In this work we combine ideas from intrinsic motivation and transfer learning.
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos.
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Our proposed AV CCA-GNN model deals with multimodal representation learning context.
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network.
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus
Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles
Depending on the resemblance of the traffic behavior at the sensed road, the generation method can be fed with data from one road only.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
The dual discriminator design aims to improve the edge information in MRI reconstruction.
Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future
Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years.
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey
Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted.
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives
In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems.
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification
Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models, namely, potential memory, temporal patterns and pixel absence effect.
Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions
In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process.
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously.
A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs
The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted.
CURIE: A Cellular Automaton for Concept Drift Detection
Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges
In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances.
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting.
A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms
In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems
Within this specific branch, approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a notable momentum when tackling multiple optimization tasks.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data.
Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples
The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance.
COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking
In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand.
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration.
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality , and the intertask relationships found and exploited over the search process.
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)
From our analysis, we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior.
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven.
LUNAR: Cellular Automata for Drifting Data Streams
A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms).
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks
The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories.
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Rank aggregation for non-stationary data streams
Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more \textit{reliable} than others and show that this generalization can solve the problem of rank aggregation over non-stationary data streams.
Spiking Neural Networks and Online Learning: An Overview and Perspectives
Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores.
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios.
jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics
This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques.
