His primary areas of study are Artificial intelligence, Natural language processing, Word, Word embedding and Word2vec. Tomas Mikolov has included themes like Analogy, Machine learning and Pattern recognition in his Artificial intelligence study. In general Natural language processing study, his work on Syntax often relates to the realm of Paragraph, thereby connecting several areas of interest.
His Syntax research is multidisciplinary, incorporating perspectives in Principle of compositionality, Word order and Softmax function. His Word research incorporates themes from Example-based machine translation, Translation, Rule-based machine translation, Machine translation and Phrase. His Word2vec study frequently involves adjacent topics like Distributional semantics.
Tomas Mikolov mainly focuses on Artificial intelligence, Natural language processing, Word, Language model and Recurrent neural network. His Artificial intelligence research focuses on Artificial neural network in particular. His studies in Natural language processing integrate themes in fields like Analogy and Feature.
Word embedding is the focus of his Word research. His work focuses on many connections between Word embedding and other disciplines, such as Word2vec, that overlap with his field of interest in Distributional semantics. His Syntax study combines topics in areas such as Principle of compositionality, Word order and Softmax function.
Tomas Mikolov spends much of his time researching Artificial intelligence, Theoretical computer science, Complex system, Word and Natural language processing. The study incorporates disciplines such as Machine learning, Forgetting and Pattern recognition in addition to Artificial intelligence. His Theoretical computer science research includes elements of Artificial chemistry and Autopoiesis.
His study on Complex system also encompasses disciplines like
Tomas Mikolov mostly deals with Artificial intelligence, Word, Natural language processing, Key and Online encyclopedia. His work on Modality and Interpretability as part of general Artificial intelligence research is frequently linked to Modal, Discretization and Focus, thereby connecting diverse disciplines of science. Tomas Mikolov merges Modal with Speedup in his study.
Convolutional neural network, Process and Pattern recognition are fields of study that intersect with his Speedup study. His Word study incorporates themes from Inference, Translation and State. Key is integrated with Hindi, Analogy and Quality in his research.
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado.
neural information processing systems (2013)
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov;Kai Chen;Greg S. Corrado;Jeffrey Dean.
arXiv: Computation and Language (2013)
Recurrent neural network based language model
Tomas Mikolov;Martin Karafiát;Lukás Burget;Jan Cernocký.
conference of the international speech communication association (2010)
Extensions of recurrent neural network language model
Tomas Mikolov;Stefan Kombrink;Lukas Burget;Jan Cernocky.
international conference on acoustics, speech, and signal processing (2011)
Enriching Word Vectors with Subword Information
Piotr Bojanowski;Edouard Grave;Armand Joulin;Tomas Mikolov.
Transactions of the Association for Computational Linguistics (2017)
Distributed Representations of Sentences and Documents
Quoc Le;Tomas Mikolov.
arXiv: Computation and Language (2014)
Linguistic Regularities in Continuous Space Word Representations
Tomas Mikolov;Wen-tau Yih;Geoffrey Zweig.
north american chapter of the association for computational linguistics (2013)
On the difficulty of training recurrent neural networks
Razvan Pascanu;Tomas Mikolov;Yoshua Bengio.
international conference on machine learning (2013)
Bag of Tricks for Efficient Text Classification
Armand Joulin;Edouard Grave;Piotr Bojanowski;Tomas Mikolov.
conference of the european chapter of the association for computational linguistics (2017)
Exploiting Similarities among Languages for Machine Translation
Tomas Mikolov;Quoc V. Le;Ilya Sutskever.
arXiv: Computation and Language (2013)
Profile was last updated on December 6th, 2021.
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