Eugene Kharitonov

1.9k citations
43 papers · 1.1k indexed · 2 hit papers · h-index 15

Eugene Kharitonov

37 papers receiving 990 citations

Hit Papers

AudioLM: A Language Modeling Approach to Au...201202020262022202450100150200250

Peers

Eugene Kharitonov
Comparison fields: 5 of 74
  • Signal Processing 416
  • Artificial Intelligence 727
  • Computer Science Applications 30
  • Computer Vision and Pattern Recognition 108
  • Experimental and Cognitive Psychology 64
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Citations per year

Countries citing papers authored by Eugene Kharitonov

Since Specialization
Citations

This map shows the geographic impact of Eugene Kharitonov'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 Eugene Kharitonov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Kharitonov more than expected).

Fields of papers citing papers by Eugene Kharitonov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eugene Kharitonov. 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 Eugene Kharitonov. The network helps show where Eugene Kharitonov may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Eugene Kharitonov, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eugene Kharitonov Line = papers co-authored together Eugene Kharitonov links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
AudioLM: A Language Modeling Approach to Audio Generationbreakdown →
2023201
2 202320
3 202354
4 202214
5 2021144
6
The Interspeech Zero Resource Speech Challenge 2021: Spoken language modelling.
20211
7
What they do when in doubt: a study of inductive biases in seq2seq learners
20210
8 202061
9 20190
10 201914
11 20192
12 20180
13 20160
14 20151
15 20155
16
Putting into practice innovative potential in the universal radial-shear rolling process
201419
17 20146
18 201316
19 20112
20 20051

About Eugene Kharitonov

Eugene Kharitonov is a scholar working on Industrial and Manufacturing Engineering, Computer Science Applications, Mechanics of Materials, Signal Processing and Artificial Intelligence, having authored 43 papers that have together received 1.1k indexed citations. Recurring topics across this work include Metallurgy and Material Forming (15 papers), Metal Alloys Wear and Properties (12 papers), Speech Recognition and Synthesis (10 papers), Diverse Industrial Engineering Technologies (9 papers), Information Retrieval and Search Behavior (8 papers), Topic Modeling (5 papers), Music and Audio Processing (5 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Signal Processing (416 citations), Artificial Intelligence (727 citations), Computer Science Applications (30 citations), Computer Vision and Pattern Recognition (108 citations) and Experimental and Cognitive Psychology (64 citations). Eugene Kharitonov has collaborated with scholars based in Russia, France and United Kingdom. Frequent co-authors include Emmanuel Dupoux, Gabriel Synnaeve, Pierre-Emmanuel Mazaré, Wei-Ning Hsu, Jade Copet, Yossi Adi, Damien Vincent, Zalán Borsos, Neil Zeghidour and Matt Sharifi. Their work appears in journals such as Transactions of the Association for Computational Linguistics, IEEE/ACM Transactions on Audio Speech and Language Processing, Proceedings of the National Academy of Sciences, Steel in Translation and Izvestiya Ferrous Metallurgy.

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.

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