Tomáš Mikolov
About
In The Last Decade
Tomáš Mikolov
39 papers receiving 26.7k citations
Hit Papers
Peers
Comparison fields: 5 of 203
- Artificial Intelligence 22.3k
- Computer Vision and Pattern Recognition 5.2k
- Information Systems 4.7k
- Signal Processing 2.1k
- Molecular Biology 1.8k
Countries citing papers authored by Tomáš Mikolov
This map shows the geographic impact of Tomáš Mikolov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tomáš Mikolov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomáš Mikolov more than expected).
Fields of papers citing papers by Tomáš Mikolov
This network shows the impact of papers produced by Tomáš Mikolov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tomáš Mikolov. The network helps show where Tomáš Mikolov may publish in the future.
Co-authorship network of co-authors of Tomáš Mikolov
This figure shows the co-authorship network connecting the top 25 collaborators of Tomáš Mikolov. A scholar is included among the top collaborators of Tomáš Mikolov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tomáš Mikolov. Tomáš Mikolov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 10 | |
| 3 | Improving Supervised Bilingual Mapping of Word Embeddings. | 1 |
| 4 | 152 | |
| 5 | Learning Simpler Language Models with the Delta Recurrent Neural Network Framework. | 3 |
| 6 | Fast Linear Model for Knowledge Graph Embeddings. | 2 |
| 7 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | 70 |
| 8 | Using Neural Networks for Modeling and Representing Natural Languages | 5 |
| 9 | Learning Longer Memory in Recurrent Neural Networks | 11 |
| 10 | 57 | |
| 11 | DeViSE: A Deep Visual-Semantic Embedding Model breakdown → | 1145 |
| 12 | Linguistic Regularities in Continuous Space Word Representations breakdown → | 1635 |
| 13 | Combining Heterogeneous Models for Measuring Relational Similarity | 29 |
| 14 | Understanding the exploding gradient problem | 216 |
| 15 | A Fast Re-scoring Strategy to Capture Long-Distance Dependencies | 25 |
| 16 | Strategies for training large scale neural network language models breakdown → | 329 |
| 17 | RNNLM - Recurrent Neural Network Language Modeling Toolkit | 165 |
| 18 | 135 | |
| 19 | PCA-based Feature Extraction for Phonotactic Language Recognition | 14 |
| 20 | Data selection and calibration issues in automatic language recognition - investigation with BUT-AGNITIO NIST LRE 2009 system. | 21 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.