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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 94 1 Towards Parallel Algorithms for Abstract Dialectical Frameworks ... Hofer, Mathias. - : TU Wien, 2022 BASE Show details 2 Detecting Signal Corruptions in Voice Recordings for Speech Therapy ; Igenkänning av Signalproblem i Röstinspelningar för Logopedi Nylén, Helmer. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021 BASE Show details 3 Noise-skipping Earley parsing and in-order tree extraction from shared packed parse forests Dohmann, Jeremy. - 2021 BASE Show details 4 Temporal Social Network Analysis using Harvard Caselaw Access Project Trias, Fernando. - 2021 BASE Show details 5 Going beyond our means: A proposal for improving psycholinguistic methods Goldshtein, Maria. - 2021 BASE Show details 6 Extracting Human Behaviour and Personality Traits from Social Media Singh, Ravinder. - 2021 BASE Show details 7 Towards provably efficient algorithms for learning neural networks ... Goel, Surbhi. - : The University of Texas at Austin, 2020 BASE Show details 8 Factions: acts of worldbuilding on social media platforms ... Little, Dana L.. - : University of Glasgow, 2020 BASE Show details 9 Parallel text index construction ... Kurpicz, Florian. - : Technische Universität Dortmund, 2020 BASE Show details 10 Understanding and generating language with abstract meaning representation Damonte, Marco. - : The University of Edinburgh, 2020 BASE Show details 11 Detección de linguaxe misóxino e xenófobo en redes sociais mediante aprendizaxe máquina Rodríguez Fernández, Laura. - 2020 BASE Show details 12 Parallel text index construction Kurpicz, Florian. - 2020 BASE Show details 13 Towards provably efficient algorithms for learning neural networks Goel, Surbhi. - 2020 Abstract: Neural networks (NNs) have seen a surge in popularity due to their unprecedented practical success in fields such as computer vision, robotics, and natural language. Developing provably efficient algorithms for learning commonly used neural network architectures continues to be a core challenge in understanding deep learning. In particular, even the problem of learning very basic architectures remains open. The underlying difficulty arises from the highly non-convex nature of the optimization problems posed by neural networks. Despite their practical success, the standard neural network training algorithms based on gradient descent (GD) and its variants have almost no provable guarantees. This necessitates a paradigm shift towards developing new principled algorithms with provable guarantees. In this thesis, we give the first set of efficient algorithms for learning commonly studied neural network architectures under minimal assumptions. In the first part of the thesis, we focus on characterizing the computational complexity of learning a single non-linear unit. We combine techniques from kernel methods and polynomial approximation to give the first dimension-efficient algorithm for learning a single ReLU (rectified linear unit), the most popular activation function, in the agnostic learning model (arbitrary noise) for any distribution on the unit sphere. We further show that if the input distribution is assumed to be Gaussian, the problem is hard. Our results unconditionally imply that GD cannot agnostically learn a single ReLU. Lastly, we show that if we relax our learning guarantee, then there is a fully polynomial time algorithm that achieves a constant factor approximation for all isotropic log-concave distributions. We further extend our results to shallow NNs. We give the first dimension efficient algorithm for learning norm-bounded one layer fully connected NNs. We subsequently show that if the marginal distribution on the input exhibits sufficient eigenvalue decay (low dimensional structure), then one-hidden-layer NNs can be learned in polynomial time in all parameters. For one hidden layer convolutional NNs, we propose a simple iterative algorithm that efficiently recovers the underlying parameters for commonly used convolutional schemes from computer vision. We further give the first polynomial time algorithm for networks with more that one hidden layer in a weaker noise model. The techniques from this work also give improved results for problems related to boolean concept learning. Lastly, we shift focus to the unsupervised learning setting through the lens of graphical models. We study Restricted Boltzmann Machines (RBMs) which are simple generative neural networks that model a probability distribution. We give the first algorithm for learning RBMs with non-negative interactions under arbitrary biases on binary as well as non-binary input. ; Computer Sciences Keyword: Efficient algorithms; Learning theory; Neural networks URL: https://doi.org/10.26153/tsw/13349 https://hdl.handle.net/2152/86398 BASE Hide details 14 Emoción, percepción, producción: un estudio psicolingüístico para detectar emociones en el habla Gibson, M. (Mark); González-Machorro, M. (Mónica). - 2020 BASE Show details 15 Language Recognition in the Sliding Window Model ... : Formale Sprachen im Sliding-Window-Modell ... Ganardi, Moses. - : Universitätsbibliothek Siegen, 2019 BASE Show details 16 System-Aware Algorithms For Machine Learning Mendler-Dünner, Celestine. - : ETH Zurich, 2019 BASE Show details 17 Seguridad del paciente: estudio de factores para su consecución Figueiredo Escribá, Carlos de. - : Universitat de Barcelona, 2019 In: TDX (Tesis Doctorals en Xarxa) (2019) BASE Show details 18 Fast machine translation on parallel and massively parallel hardware Bogoychev, Nikolay Veselinov. - : The University of Edinburgh, 2019 BASE Show details 19 Preference inference based on lexicographic and Pareto models George, Anne-Marie. - : University College Cork, 2019 BASE Show details 20 Seguridad del paciente: estudio de factores para su consecución Figueiredo Escribá, Carlos de. - : Universitat de Barcelona, 2019 BASE Show details Page: 1 2 3 4 5

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