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HEДокторантска мрежа2023–2026

LEMUR · Learning with Multiple Representations

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2023-01-01 → 2026-12-31
Финансиране от ЕС
2 592 785 €
Участници
20
Схема
HORIZON-TMA-MSCA-DN

Линиите свързват координатора с партньорите.

Накратко на български

Машинното обучение с множество представителства изследва как данните могат да се представят по различни начини, например чрез графи или вграждания. Това помага за по-доброто разбиране на сложните данни и подобрява работата на системите за търсене на информация и управление на инфраструктури.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Learning with Multiple Representations

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g., as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to develop the theoretical and algorithmic foundations for a new paradigm in machine learning, which we call learning with multiple representations (LMR). Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. Consequently, the doctoral network is structured along three facets: theory, algorithms, and applications. The objective of the work under the theory facet is to develop the first set of formal guarantees and limitations of LMR. Here, the project focuses in particular on questions pertaining to performance prediction, computational complexity and uncertainty. The second facet of the project, algorithms, is addressed by developing unsupervised machine learning approaches for visualizing multi-modal data (e.g., knowledge graphs in graph and embedding representations), supervised explainable machine learning approaches for structured data and neuro-symbolic machine learning approaches. The last facet consist of works in information retrieval, critical infrastructure management and ethical machine learning.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g., as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g., suitable for explainability, fairness) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз