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Search results for: NEURAL EMBEDDINGS
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Towards semantic-rich word embeddings
PublicationIn recent years, word embeddings have been shown to improve the performance in NLP tasks such as syntactic parsing or sentiment analysis. While useful, they are problematic in representing ambiguous words with multiple meanings, since they keep a single representation for each word in the vocabulary. Constructing separate embeddings for meanings of ambiguous words could be useful for solving the Word Sense Disambiguation (WSD)...
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Graph Vertex Embeddings: Distance, Regularization and Community Detection
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublicationThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
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Bożena Kostek prof. dr hab. inż.
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Dirichlet-to-Neumann and Neumann-to-Dirichlet embedding methods for bound states of the Schrodinger equation.
PublicationPrzeformułowano metodę Inglesfielda, stosowaną do obliczania własności stanów związanych równania Schrodingera, stosując formalizm operatorów całkowych Dirichleta-do-Neumanna(DtN) i Neumanna-do-Dirichleta (NtD). Wykorzystano zasady wariacyjne dla energii dopuszczające użycie funkcji próbnych nieciągłych wraz z pochodnymi. Podano metodę konstrukcji jąder operatorów DtN i NtD za pomocą rozwiązań zagadnienia własnego typu Steklova....
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Dirichlet-to-Neumann and Neumann-to-Dirichlet embedding methods for bound states of the Dirac equation
PublicationZaprezentowano uogólnienie formalizmu operatorów Dirichleta-Neumanna (DtN) i Neumanna-Dirichleta (NtD) na przypadek równania Diraca. Przedstawiono zastosowanie tego formalizmu do znajdowania poziomów energetycznych cząstki Diraca związanej w potencjale.
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Silica In Silico: A Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
PublicationA novel procedure for the application of atom probe tomography (APT) to the structural analysis of biological systems, has been recently proposed, whereby the specimen is embedded by a silica matrix and ablated by a pulsed laser source. Such a technique, requires that the silica primer be properly inert and bio-compatible, keeping the native structural features of the system at hand, while condensing into an amorphous, glass-like...
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Towards neural knowledge DNA
PublicationIn this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...
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Neural networks and deep learning
PublicationIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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Neural Development
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