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Tomás Mikolov
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2020 – today
- 2024
- [c48]David Herel, Tomás Mikolov:
Collapse of Self-trained Language Models. Tiny Papers @ ICLR 2024 - [i38]Shervin Minaee, Tomás Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao:
Large Language Models: A Survey. CoRR abs/2402.06196 (2024) - [i37]David Herel, Tomás Mikolov:
Collapse of Self-trained Language Models. CoRR abs/2404.02305 (2024) - [i36]David Herel, Tomás Mikolov:
Thinking Tokens for Language Modeling. CoRR abs/2405.08644 (2024) - [i35]David Herel, Vojtech Bartek, Tomás Mikolov:
Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time. CoRR abs/2409.13338 (2024) - 2023
- [c47]David Herel
, Hugo Cisneros
Preserving Semantics in Textual Adversarial Attacks. ECAI 2023: 1036-1043 - [i34]David Herel, Tomás Mikolov:
Advancing State of the Art in Language Modeling. CoRR abs/2312.03735 (2023) - 2022
- [j6]Barbora Hudcová, Tomás Mikolov:
Classification of Discrete Dynamical Systems Based on Transients. Artif. Life 27(3-4): 220-245 (2022) - [j5]Germán Kruszewski, Tomás Mikolov:
Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry. Artif. Life 27(3-4): 277-299 (2022) - [c46]Hugo Cisneros, Tomás Mikolov, Josef Sivic:
Benchmarking Learning Efficiency in Deep Reservoir Computing. CoLLAs 2022: 532-547 - [i33]David Herel, Dominika Zogatova, Matej Kripner, Tomás Mikolov:
Emergence of Novelty in Evolutionary Algorithms. CoRR abs/2207.04857 (2022) - [i32]Hugo Cisneros
Benchmarking Learning Efficiency in Deep Reservoir Computing. CoRR abs/2210.02549 (2022) - [i31]David Herel, Hugo Cisneros, Tomás Mikolov:
Preserving Semantics in Textual Adversarial Attacks. CoRR abs/2211.04205 (2022) - 2021
- [c45]Tomás Mikolov:
Language Modeling and Artificial Intelligence. Interspeech 2021 - [c44]Barbora Hudcová, Tomás Mikolov:
Computational Hierarchy of Elementary Cellular Automata. ALIFE 2021: 105 - [i30]Germán Kruszewski, Tomás Mikolov:
Emergence of self-reproducing metabolisms as recursive algorithms in an Artificial Chemistry. CoRR abs/2103.08245 (2021) - [i29]Hugo Cisneros, Josef Sivic, Tomás Mikolov:
Visualizing computation in large-scale cellular automata. CoRR abs/2104.01008 (2021) - [i28]Barbora Hudcová, Tomás Mikolov:
Computational Hierarchy of Elementary Cellular Automata. CoRR abs/2108.00415 (2021) - [i27]Barbora Hudcová, Tomás Mikolov:
Classification of Discrete Dynamical Systems Based on Transients. CoRR abs/2108.01573 (2021) - 2020
- [j4]Dagmar Monett, Colin W. P. Lewis, Kristinn R. Thórisson, Joscha Bach, Gianluca Baldassarre, Giovanni Granato, Istvan S. N. Berkeley, François Chollet, Matthew Crosby, Henry Shevlin, John F. Sowa, John E. Laird
Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. J. Artif. Gen. Intell. 11(2): 1-100 (2020) - [c43]Hugo Cisneros
Visualizing computation in large-scale cellular automata. ALIFE 2020: 239-247 - [c42]Barbora Hudcová
Classification of Complex Systems Based on Transients. ALIFE 2020: 367-375 - [c41]Germán Kruszewski, Tomás Mikolov:
Combinatory Chemistry: Towards a Simple Model of Emergent Evolution. ALIFE 2020: 411-419 - [i26]Germán Kruszewski, Tomás Mikolov:
Combinatory Chemistry: Towards a Simple Model of Emergent Evolution. CoRR abs/2003.07916 (2020) - [i25]Germán Kruszewski, Ionut-Teodor Sorodoc, Tomás Mikolov:
Class-Agnostic Continual Learning of Alternating Languages and Domains. CoRR abs/2004.03340 (2020) - [i24]Barbora Hudcová
Classification of Complex Systems Based on Transients. CoRR abs/2008.13503 (2020)
2010 – 2019
- 2019
- [c40]Hugo Cisneros
Evolving Structures in Complex Systems. SSCI 2019: 230-237 - [c39]Carl Yang, Do Huy Hoang, Tomás Mikolov, Jiawei Han:
Place Deduplication with Embeddings. WWW 2019: 3420-3426 - [i23]Carl Yang, Do Huy Hoang, Tomás Mikolov, Jiawei Han:
Place Deduplication with Embeddings. CoRR abs/1910.04861 (2019) - [i22]Piotr Bojanowski, Onur Celebi, Tomás Mikolov, Edouard Grave, Armand Joulin:
Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment. CoRR abs/1910.06241 (2019) - [i21]Hugo Cisneros
Evolving Structures in Complex Systems. CoRR abs/1911.01086 (2019) - 2018
- [c38]Douwe Kiela, Edouard Grave, Armand Joulin, Tomás Mikolov:
Efficient Large-Scale Multi-Modal Classification. AAAI 2018: 5198-5204 - [c37]Armand Joulin, Piotr Bojanowski, Tomás Mikolov, Hervé Jégou, Edouard Grave:
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion. EMNLP 2018: 2979-2984 - [c36]Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, Tomás Mikolov:
Learning Word Vectors for 157 Languages. LREC 2018 - [c35]Tomás Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin:
Advances in Pre-Training Distributed Word Representations. LREC 2018 - [i20]Douwe Kiela, Edouard Grave, Armand Joulin, Tomás Mikolov:
Efficient Large-Scale Multi-Modal Classification. CoRR abs/1802.02892 (2018) - [i19]Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, Tomás Mikolov:
Learning Word Vectors for 157 Languages. CoRR abs/1802.06893 (2018) - [i18]Armand Joulin, Piotr Bojanowski, Tomás Mikolov, Edouard Grave:
Improving Supervised Bilingual Mapping of Word Embeddings. CoRR abs/1804.07745 (2018) - 2017
- [j3]Alexander G. Ororbia II, Tomás Mikolov, David Reitter:
Learning Simpler Language Models with the Differential State Framework. Neural Comput. 29(12) (2017) - [j2]Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomás Mikolov:
Enriching Word Vectors with Subword Information. Trans. Assoc. Comput. Linguistics 5: 135-146 (2017) - [c34]Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov:
Fast Linear Model for Knowledge Graph Embeddings. AKBC@NIPS 2017 - [c33]Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomás Mikolov:
Bag of Tricks for Efficient Text Classification. EACL (2) 2017: 427-431 - [c32]Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomás Mikolov:
CommAI: Evaluating the first steps towards a useful general AI. ICLR (Workshop) 2017 - [c31]Yacine Jernite, Edouard Grave, Armand Joulin, Tomás Mikolov:
Variable Computation in Recurrent Neural Networks. ICLR (Poster) 2017 - [i17]Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomás Mikolov:
CommAI: Evaluating the first steps towards a useful general AI. CoRR abs/1701.08954 (2017) - [i16]Alexander G. Ororbia II, Tomás Mikolov, David Reitter:
Learning Simpler Language Models with the Delta Recurrent Neural Network Framework. CoRR abs/1703.08864 (2017) - [i15]Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomás Mikolov:
Fast Linear Model for Knowledge Graph Embeddings. CoRR abs/1710.10881 (2017) - [i14]Tomás Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin:
Advances in Pre-Training Distributed Word Representations. CoRR abs/1712.09405 (2017) - 2016
- [c30]Tomás Mikolov, Armand Joulin, Marco Baroni
A Roadmap Towards Machine Intelligence. CICLing (1) 2016: 29-61 - [c29]Wojciech Zaremba, Tomás Mikolov, Armand Joulin, Rob Fergus:
Learning Simple Algorithms from Examples. ICML 2016: 421-429 - [c28]Jason Weston, Antoine Bordes, Sumit Chopra, Tomás Mikolov:
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks. ICLR (Poster) 2016 - [i13]Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomás Mikolov:
Bag of Tricks for Efficient Text Classification. CoRR abs/1607.01759 (2016) - [i12]Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomás Mikolov:
Enriching Word Vectors with Subword Information. CoRR abs/1607.04606 (2016) - [i11]Yacine Jernite, Edouard Grave, Armand Joulin, Tomás Mikolov:
Variable Computation in Recurrent Neural Networks. CoRR abs/1611.06188 (2016) - [i10]Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hervé Jégou, Tomás Mikolov:
FastText.zip: Compressing text classification models. CoRR abs/1612.03651 (2016) - 2015
- [c27]Armand Joulin, Tomás Mikolov:
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets. NIPS 2015: 190-198 - [c26]Grégoire Mesnil, Tomás Mikolov, Marc'Aurelio Ranzato, Yoshua Bengio:
Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews. ICLR (Workshop) 2015 - [c25]Tomás Mikolov, Armand Joulin, Sumit Chopra, Michaël Mathieu, Marc'Aurelio Ranzato:
Learning Longer Memory in Recurrent Neural Networks. ICLR (Workshop) 2015 - [i9]Armand Joulin, Tomás Mikolov:
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets. CoRR abs/1503.01007 (2015) - [i8]Piotr Bojanowski, Armand Joulin, Tomás Mikolov:
Alternative structures for character-level RNNs. CoRR abs/1511.06303 (2015) - [i7]Wojciech Zaremba, Tomás Mikolov, Armand Joulin, Rob Fergus:
Learning Simple Algorithms from Examples. CoRR abs/1511.07275 (2015) - [i6]Tomás Mikolov, Armand Joulin, Marco Baroni:
A Roadmap towards Machine Intelligence. CoRR abs/1511.08130 (2015) - 2014
- [c24]Tomás Mikolov:
Using Neural Networks for Modeling and Representing Natural Languages. COLING (Tutorials) 2014: 3-4 - [c23]Quoc V. Le, Tomás Mikolov:
Distributed Representations of Sentences and Documents. ICML 2014: 1188-1196 - [c22]Ciprian Chelba, Tomás Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, Tony Robinson:
One billion word benchmark for measuring progress in statistical language modeling. INTERSPEECH 2014: 2635-2639 - [c21]Mohammad Norouzi, Tomás Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean:
Zero-Shot Learning by Convex Combination of Semantic Embeddings. ICLR 2014 - [i5]Quoc V. Le, Tomás Mikolov:
Distributed Representations of Sentences and Documents. CoRR abs/1405.4053 (2014) - 2013
- [j1]Anoop Deoras, Tomás Mikolov, Stefan Kombrink, Kenneth Church
Approximate inference: A sampling based modeling technique to capture complex dependencies in a language model. Speech Commun. 55(1): 162-177 (2013) - [c20]Razvan Pascanu, Tomás Mikolov, Yoshua Bengio:
On the difficulty of training recurrent neural networks. ICML (3) 2013: 1310-1318 - [c19]Tomás Mikolov, Wen-tau Yih, Geoffrey Zweig:
Linguistic Regularities in Continuous Space Word Representations. HLT-NAACL 2013: 746-751 - [c18]Alisa Zhila, Wen-tau Yih, Christopher Meek, Geoffrey Zweig, Tomás Mikolov:
Combining Heterogeneous Models for Measuring Relational Similarity. HLT-NAACL 2013: 1000-1009 - [c17]Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, Tomás Mikolov:
DeViSE: A Deep Visual-Semantic Embedding Model. NIPS 2013: 2121-2129 - [c16]Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, Jeffrey Dean:
Distributed Representations of Words and Phrases and their Compositionality. NIPS 2013: 3111-3119 - [c15]Tomás Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean:
Efficient Estimation of Word Representations in Vector Space. ICLR (Workshop Poster) 2013 - [i4]Tomás Mikolov, Quoc V. Le, Ilya Sutskever:
Exploiting Similarities among Languages for Machine Translation. CoRR abs/1309.4168 (2013) - [i3]Tomás Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean:
Distributed Representations of Words and Phrases and their Compositionality. CoRR abs/1310.4546 (2013) - [i2]Ciprian Chelba, Tomás Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn:
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling. CoRR abs/1312.3005 (2013) - 2012
- [c14]Stefan Kombrink, Tomás Mikolov, Martin Karafiát
Improving language models for ASR using translated in-domain data. ICASSP 2012: 4405-4408 - [c13]Tomás Mikolov, Geoffrey Zweig:
Context dependent recurrent neural network language model. SLT 2012: 234-239 - [i1]Razvan Pascanu, Tomás Mikolov, Yoshua Bengio:
Understanding the exploding gradient problem. CoRR abs/1211.5063 (2012) - 2011
- [c12]Tomás Mikolov, Anoop Deoras, Daniel Povey, Lukás Burget
Strategies for training large scale neural network language models. ASRU 2011: 196-201 - [c11]Anoop Deoras, Tomás Mikolov, Kenneth Church:
A Fast Re-scoring Strategy to Capture Long-Distance Dependencies. EMNLP 2011: 1116-1127 - [c10]Tomás Mikolov, Stefan Kombrink, Lukás Burget
Extensions of recurrent neural network language model. ICASSP 2011: 5528-5531 - [c9]Anoop Deoras, Tomás Mikolov, Stefan Kombrink, Martin Karafiát
Variational approximation of long-span language models for lvcsr. ICASSP 2011: 5532-5535 - [c8]Tomás Mikolov, Anoop Deoras, Stefan Kombrink, Lukás Burget, Jan Cernocký:
Empirical Evaluation and Combination of Advanced Language Modeling Techniques. INTERSPEECH 2011: 605-608 - [c7]Stefan Kombrink, Tomás Mikolov, Martin Karafiát, Lukás Burget:
Recurrent Neural Network Based Language Modeling in Meeting Recognition. INTERSPEECH 2011: 2877-2880 - 2010
- [c6]Tomás Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, Sanjeev Khudanpur
Recurrent neural network based language model. INTERSPEECH 2010: 1045-1048 - [c5]Zdenek Jancik, Oldrich Plchot, Niko Brümmer, Lukás Burget, Ondrej Glembek, Valiantsina Hubeika, Martin Karafiát, Pavel Matejka, Tomás Mikolov, Albert Strasheim, Jan Cernocký:
Data selection and calibration issues in automatic language recognition - investigation with BUT-AGNITIO NIST LRE 2009 system. Odyssey 2010: 37 - [c4]Tomás Mikolov, Oldrich Plchot, Ondrej Glembek, Lukás Burget, Jan Cernocký:
PCA-based Feature Extraction for Phonotactic Language Recognition. Odyssey 2010: 42
2000 – 2009
- 2009
- [c3]Tomás Mikolov, Jirí Kopecký, Lukás Burget
Neural network based language models for highly inflective languages. ICASSP 2009: 4725-4728 - 2008
- [c2]Pavel Matejka, Lukás Burget, Ondrej Glembek, Petr Schwarz, Valiantsina Hubeika, Michal Fapso, Tomás Mikolov, Oldrich Plchot, Jan Cernocký:
BUT language recognition system for NIST 2007 evaluations. INTERSPEECH 2008: 739-742 - [c1]Ondrej Glembek, Pavel Matejka, Lukás Burget, Tomás Mikolov:
Advances in phonotactic language recognition. INTERSPEECH 2008: 743-746
Coauthor Index
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last updated on 2024-10-18 19:32 CEST by the dblp team
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