Prossimi seminari del Dipartimento di Matematica

After an introduction to the notion of quantum generative adversarial networks (qGANs), I will summarize a recent quantum tomography protocol for constructing a classical estimate of a quantum state by performing repeated measurements on a n-qubit system. I will then discuss the convergence of the protocol with respect to a quantum version of the first-order Wasserstein distance, inspired by the theory of optimal mass transport. In particular, I will show how this convergence result allows us to conclude that a qGAN can be equivalently trained using classical estimators of quantum states instead of quantum data. This fact is important in practice, as it enables the training of quantum models without requiring direct access to quantum memory or coherent quantum data streams.

Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question is how do they compare in capturing the information contained in networked datasets? We address this objective by solving a link prediction task and we conduct three main experiments, on both synthetic and real networks: one focuses on how PGMs and GNNs handle input features, while the other two investigate their robustness to noisy features and increasing heterophily of the graph. PGMs do not necessarily require features on nodes, while GNNs cannot exploit the network edges alone, and the choice of input features matters. We find that GNNs are outperformed by PGMs when input features are low-dimensional or noisy, mimicking many real scenarios where node attributes might be scalar or noisy. Then, we find that PGMs are more robust than GNNs when the heterophily of the graph is increased. Finally, to assess performance beyond prediction tasks, we also compare the two frameworks in terms of their computational complexity and interpretability.

A nonlinear matrix is a matrix whose entries are nonlinear functions of a parameter. Such problems arise from physics (Schroedinger equation) and mechanical engineering (porous materials, boundary element method, e.g.). The last 20 years, rational approximation methods and linearization were proposed to approximate such matrices by linear pencils of much higher dimensions. Applications are the nonlinear eigenvalue problem, parametric linear systems, frequency sweeping, model order reduction and the solution of time dependent problems with nonlinear frequency dependencies. We give an overview of approximation methods with focus on AAA and Krylov methods that exploit the structure of the linear pencil.

Note: this is the first part of a two-part seminar. AI is progressing at a remarkable speed, but we still don’t have a clear understanding of basic concepts in cognition (e.g. learning, understanding, abstraction, awareness, intelligence, etc). I shall argue that research focused on understanding how learning machines such as LLMs or deep neural networks do what they do, sidesteps the key issue by defining these concepts from the outset. For example, statistical learning is based on a classification of problems (supervised/unsupervised, classification, regression etc.) and addresses the resulting optimisation problem (maximisation of the likelihood, minimisation of errors, etc). Learning entails first of all detecting what makes sense to be learned 1) from very few samples, and 2) without a priori knowing why that date makes sense. This requires a quantitative notion of relevance that can distinguish data that makes sense from meaningless noise. I will first introduce and discuss the notion of relevance. Next I will claim that learning differs from understanding, where the latter implies integrating data that make sense into a pre-existing representation. The properties of this representation should be abstract, i.e. independent of the data, precisely because they need to represent data of a widely different domain. This is what enables higher cognitive functions that we do all the time, like drawing analogies and relating data learned independently from widely different domains. Such a representation should be flexible and continuously adaptable if more data or more resources are made available. I will show that such an abstract representation can be defined as the fixed point of a renormalisation group transformation, and it coincides with a model that can be defined from the principle of maximal relevance. I will provide empirical evidence that the representations of simple neural networks approach this universal model as the network is trained on a broader and broader domain of data. Overall, the aim of the seminar is to support the idea that an approach to central issues in cognition is also possible studying very simple models and does not necessarily require understanding large machine learning models.

Note: this is the second part of a two-part seminar. AI is progressing at a remarkable speed, but we still don’t have a clear understanding of basic concepts in cognition (e.g. learning, understanding, abstraction, awareness, intelligence, etc). I shall argue that research focused on understanding how learning machines such as LLMs or deep neural networks do what they do, sidesteps the key issue by defining these concepts from the outset. For example, statistical learning is based on a classification of problems (supervised/unsupervised, classification, regression etc.) and addresses the resulting optimisation problem (maximisation of the likelihood, minimisation of errors, etc). Learning entails first of all detecting what makes sense to be learned 1) from very few samples, and 2) without a priori knowing why that date makes sense. This requires a quantitative notion of relevance that can distinguish data that makes sense from meaningless noise. I will first introduce and discuss the notion of relevance. Next I will claim that learning differs from understanding, where the latter implies integrating data that make sense into a pre-existing representation. The properties of this representation should be abstract, i.e. independent of the data, precisely because they need to represent data of a widely different domain. This is what enables higher cognitive functions that we do all the time, like drawing analogies and relating data learned independently from widely different domains. Such a representation should be flexible and continuously adaptable if more data or more resources are made available. I will show that such an abstract representation can be defined as the fixed point of a renormalisation group transformation, and it coincides with a model that can be defined from the principle of maximal relevance. I will provide empirical evidence that the representations of simple neural networks approach this universal model as the network is trained on a broader and broader domain of data. Overall, the aim of the seminar is to support the idea that an approach to central issues in cognition is also possible studying very simple models and does not necessarily require understanding large machine learning models.