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2. Instance selection for dynamic algorithm configuration with reinforcement learning : improving generalizationCarolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer, 2024, objavljeni znanstveni prispevek na konferenci Ključne besede: dynamic algorithm configuration, reinforcement learning, instance selection, generalization
Objavljeno v DiRROS: 16.09.2024; Ogledov: 719; Prenosov: 192
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4. Zero-shot evaluation of ChatGPT for food named-entity recognition and linkingMatevž Ogrinc, Barbara Koroušić-Seljak, Tome Eftimov, 2024, izvirni znanstveni članek Povzetek: Introduction: Recognizing and extracting key information from textual data plays an important role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed decision-making, question-answering, and more. It is especially apparent in the food domain, where critical information guides the decisions of nutritionists and clinicians. The information extraction process involves two natural language processing tasks named entity recognition—NER and named entity linking—NEL. With the emergence of large language models (LLMs), especially ChatGPT, many areas began incorporating its knowledge to reduce workloads or simplify tasks. In the field of food, however, we noticed an opportunity to involve ChatGPT in NER and NEL.
Methods: To assess ChatGPT's capabilities, we have evaluated its two versions, ChatGPT-3.5 and ChatGPT-4, focusing on their performance across both NER and NEL tasks, emphasizing food-related data. To benchmark our results in the food domain, we also investigated its capabilities in a more broadly investigated biomedical domain. By evaluating its zero-shot capabilities, we were able to ascertain the strengths and weaknesses of the two versions of ChatGPT.
Results: Despite being able to show promising results in NER compared to other models. When tasked with linking entities to their identifiers from semantic models ChatGPT's effectiveness falls drastically.
Discussion: While the integration of ChatGPT holds potential across various fields, it is crucial to approach its use with caution, particularly in relying on its responses for critical decisions in food and bio-medicine.
Ključne besede: ChatGPT, food data, named-entity recognition, named-entity linking
Objavljeno v DiRROS: 16.09.2024; Ogledov: 114; Prenosov: 56
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5. Generalization ability of feature-based performance prediction models : a statistical analysis across benchmarksAna Nikolikj, Ana Kostovska, Gjorgjina Cenikj, Carola Doerr, Tome Eftimov, 2024, objavljeni znanstveni prispevek na konferenci Povzetek: This study examines the generalization ability of algorithm performance prediction models across various bench-mark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances.
Ključne besede: meta-learning, single-objective optimization, module importance
Objavljeno v DiRROS: 16.09.2024; Ogledov: 156; Prenosov: 77
Celotno besedilo (1,29 MB)
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7. A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimizationGjorgjina Cenikj, Gašper Petelin, Tome Eftimov, 2024, izvirni znanstveni članek Povzetek: The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models’ generalizability in single-objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer-based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single-best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance; yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability.
Ključne besede: algorithm selection, multi-target regression, generalization, benchmarking
Objavljeno v DiRROS: 21.05.2024; Ogledov: 413; Prenosov: 462
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8. NutriGreen image dataset : a collection of annotated nutrition, organic, and vegan food productsJan Drole, Igor Pravst, Tome Eftimov, Barbara Koroušić-Seljak, 2024, izvirni znanstveni članek Povzetek: In this research, we introduce the NutriGreen dataset, which is a collection of images representing branded food products aimed for training segmentation models for detecting various labels on food packaging. Each image in the dataset comes with three distinct labels: one indicating its nutritional quality using the Nutri-Score, another denoting whether it is vegan or vegetarian origin with the V-label, and a third displaying the EU organic certification (BIO) logo.
Objavljeno v DiRROS: 23.04.2024; Ogledov: 448; Prenosov: 166
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10. PS-AAS : portfolio selection for automated algorithm selection in black-box optimizationAna Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Janković, Ana Nikolikj, Urban Škvorc, Peter Korošec, Carola Doerr, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: automated algorithm selection, portfolio selection, black box optimization
Objavljeno v DiRROS: 11.12.2023; Ogledov: 718; Prenosov: 278
Celotno besedilo (1,90 MB)
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