Examinando por Autor "Ser Lorente, Javier del"

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    Managing the unknown in machine learning: definitions, related areas, recent advances, and prospects
    (Elsevier B.V., 2024-06-14) Barcina Blanco, Marcos; López Lobo, Jesús; García Bringas, Pablo; Ser Lorente, Javier del
    In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen circumstances and novel data types is of paramount importance. The deployment of Artificial Intelligence is progressively aimed at more realistic and open scenarios where data, tasks, and conditions are variable and not fully predetermined, and therefore where a closed set assumption cannot be hold. In such evolving environments, machine learning is asked to be autonomous, continuous, and adaptive, requiring effective management of uncertainty and the unknown to fulfill expectations. In response, there is a vigorous effort to develop a new generation of models, which are characterized by enhanced autonomy and a broad capacity to generalize, enabling them to perform effectively across a wide range of tasks. The field of machine learning in open set environments poses many challenges and also brings together different paradigms, some traditional but others emerging, where the overlapping and confusion between them makes it difficult to distinguish them or give them the necessary relevance. This work delves into the frontiers of methodologies that thrive in these open set environments, by identifying common practices, limitations, and connections between the paradigms Open-Ended Learning, Open-World Learning, Open Set Recognition, and other related areas such as Continual Learning, Out-of-Distribution detection, Novelty Detection, and Active Learning. We seek to easy the understanding of these fields and their common roots, uncover open problems and suggest several research directions that may motivate and articulate future efforts towards more robust and autonomous systems.
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    On the black-box explainability of object detection models for safe and trustworthy industrial applications
    (Elsevier B.V., 2024-12) Andrés Fernández, Alain; Martínez Seras, Aitor; Laña Aurrecoechea, Ibai; Ser Lorente, Javier del
    In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstanding results and are widely used in critical domains like autonomous driving and video surveillance. However, their adoption in high-risk applications, where errors may cause severe consequences, remains limited. Explainable Artificial Intelligence methods aim to address this issue, but many existing techniques are model-specific and designed for classification tasks, making them less effective for object detection and difficult for non-specialists to interpret. In this work we focus on model-agnostic explainability methods for object detection models and propose D-MFPP, an extension of the Morphological Fragmental Perturbation Pyramid (MFPP) technique based on segmentation-based masks to generate explanations. Additionally, we introduce D-Deletion, a novel metric combining faithfulness and localization, adapted specifically to meet the unique demands of object detectors. We evaluate these methods on real-world industrial and robotic datasets, examining the influence of parameters such as the number of masks, model size, and image resolution on the quality of explanations. Our experiments use single-stage object detection models applied to two safety-critical robotic environments: i) a shared human-robot workspace where safety is of paramount importance, and ii) an assembly area of battery kits, where safety is critical due to the potential for damage among high-risk components. Our findings evince that D-Deletion effectively gauges the performance of explanations when multiple elements of the same class appear in a scene, while D-MFPP provides a promising alternative to D-RISE when fewer masks are used.
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    On the improvement of generalization and stability of forward-only learning via neural polarization
    (IOS Press BV, 2024-10-16) Terres Escudero, Erik B.; Ser Lorente, Javier del; García Bringas, Pablo
    Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional contrastive forward pass. Among these approaches, the so-called Forward-Forward Algorithm (FFA) has been shown to achieve competitive levels of performance in terms of generalization and complexity. Networks trained using FFA learn to contrastively maximize a layer-wise defined goodness score when presented with real data (denoted as positive samples) and to minimize it when processing synthetic data (corr. negative samples). 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. To overcome this issue, in this work we propose a novel implementation of the FFA algorithm, denoted as Polar-FFA, which extends the original formulation by introducing a neural division (polarization) between positive and negative instances. Neurons in each of these groups aim to maximize their goodness when presented with their respective data type, thereby creating a symmetric gradient behavior. To empirically gauge the improved learning capabilities of our proposed Polar-FFA, we perform several systematic experiments using different activation and goodness functions over image classification datasets. Our results demonstrate that Polar-FFA outperforms FFA in terms of accuracy and convergence speed. Furthermore, its lower reliance on hyperparameters reduces the need for hyperparameter tuning to guarantee optimal generalization capabilities, thereby allowing for a broader range of neural network configurations.
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    PADL: a modeling and deployment language for advanced analytical services
    (MDPI AG, 2020-11-24) Díaz de Arcaya Serrano, Josu; Miñón Jiménez, Raúl; Torre Bastida, Ana Isabel; Ser Lorente, Javier del; Almeida, Aitor
    In the smart city context, Big Data analytics plays an important role in processing the data collected through IoT devices. The analysis of the information gathered by sensors favors the generation of specific services and systems that not only improve the quality of life of the citizens, but also optimize the city resources. However, the difficulties of implementing this entire process in real scenarios are manifold, including the huge amount and heterogeneity of the devices, their geographical distribution, and the complexity of the necessary IT infrastructures. For this reason, the main contribution of this paper is the PADL description language, which has been specifically tailored to assist in the definition and operationalization phases of the machine learning life cycle. It provides annotations that serve as an abstraction layer from the underlying infrastructure and technologies, hence facilitating the work of data scientists and engineers. Due to its proficiency in the operationalization of distributed pipelines over edge, fog, and cloud layers, it is particularly useful in the complex and heterogeneous environments of smart cities. For this purpose, PADL contains functionalities for the specification of monitoring, notifications, and actuation capabilities. In addition, we provide tools that facilitate its adoption in production environments. Finally, we showcase the usefulness of the language by showing the definition of PADL-compliant analytical pipelines over two uses cases in a smart city context (flood control and waste management), demonstrating that its adoption is simple and beneficial for the definition of information and process flows in such environments.