66 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    Generative adversarial networks for sequential learning

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    Generative modelling aims to learn the data generating mechanism from observations without supervision. It is a desirable and natural approach for learning unlabelled data which is easily accessible. Deep generative models refer to a class of generative models combined with the usage of deep learning techniques, taking advantage of the intuitive principles of generative models as well as the expressiveness and flexibility of neural networks. The applications of generative modelling include image, audio, and video synthesis, text summarisation and translation, and so on. The methods developed in this thesis particularly emphasise on domains involving data of sequential nature, such as video generation and prediction, weather forecasting, and dynamic 3D reconstruction. Firstly, we introduce a new adversarial algorithm for training generative models suitable for sequential data. This algorithm is built on the theory of Causal Optimal Transport (COT) which constrains the transport plans to respect the temporal dependencies exhibited in the data. Secondly, the algorithm is extended to learn conditional sequences, that is, how a sequence is likely to evolve given the observation of its past evolution. Meanwhile, we work with the modified empirical measures to guarantee the convergence of the COT distance when the sequences do not overlap at any time step. Thirdly, we show that state-of-the-art results in the complex spatio-temporal modelling using GANs can be further improved by leveraging prior knowledge in the spatial-temporal correlation in the domain of weather forecasting. Finally, we demonstrate how deep generative models can be adopted to address a classical statistical problem of conditional independence testing. A class of classic approaches for such a task requires computing a test statistic using samples drawn from two unknown conditional distributions. We therefore present a double GANs framework to learn two generative models that approximate both conditional distributions. The success of this approach sheds light on how certain challenging statistical problems can benefit from the adequate learning results as well as the efficient sampling procedure of deep generative models

    SIS 2017. Statistics and Data Science: new challenges, new generations

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    The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

    Three Risky Decades: A Time for Econophysics?

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    Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

    Solving correlation clustering with QAOA and a Rydberg qudit system: a full-stack approach

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    We study the correlation clustering problem using the quantum approximate optimization algorithm (QAOA) and qudits, which constitute a natural platform for such non-binary problems. Specifically, we consider a neutral atom quantum computer and propose a full stack approach for correlation clustering, including Hamiltonian formulation of the algorithm, analysis of its performance, identification of a suitable level structure for 87Sr{}^{87}{\rm Sr} and specific gate design. We show the qudit implementation is superior to the qubit encoding as quantified by the gate count. For single layer QAOA, we also prove (conjecture) a lower bound of 0.63670.6367 (0.66990.6699) for the approximation ratio on 3-regular graphs. Our numerical studies evaluate the algorithm's performance by considering complete and Erd\H{o}s-R\'enyi graphs of up to 7 vertices and clusters. We find that in all cases the QAOA surpasses the Swamy bound 0.76660.7666 for the approximation ratio for QAOA depths p≥2p \geq 2. Finally, by analysing the effect of errors when solving complete graphs we find that their inclusion severely limits the algorithm's performance.Comment: 22 + 11 page

    Machine Learning

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    Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
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