Morgane Rivière

2.6k total citations · 1 hit paper
8 papers, 570 citations indexed

About

Morgane Rivière is a scholar working on Artificial Intelligence, Signal Processing and Materials Chemistry. According to data from OpenAlex, Morgane Rivière has authored 8 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Signal Processing and 2 papers in Materials Chemistry. Recurrent topics in Morgane Rivière's work include Speech Recognition and Synthesis (4 papers), Music and Audio Processing (3 papers) and Speech and Audio Processing (2 papers). Morgane Rivière is often cited by papers focused on Speech Recognition and Synthesis (4 papers), Music and Audio Processing (3 papers) and Speech and Audio Processing (2 papers). Morgane Rivière collaborates with scholars based in United States, Israel and France. Morgane Rivière's co-authors include Aini Palizhati, C. Lawrence Zitnick, Weihua Hu, Siddharth Goyal, Zachary W. Ulissi, Brandon M. Wood, Abhishek Das, Kevin Tran, Javier Heras‐Domingo and Anuroop Sriram and has published in prestigious journals such as ACS Catalysis, IEEE Transactions on Image Processing and King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology).

In The Last Decade

Morgane Rivière

8 papers receiving 549 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Open Catalyst 2020 (OC20) Dataset and Community Challenges

2021 440 citations Abhishek Das, Siddharth Goyal et al. ACS Catalysis profile →

Peers

Countries citing papers authored by Morgane Rivière

Since Specialization

Citations

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

Fields of papers citing papers by Morgane Rivière

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Morgane Rivière

This figure shows the co-authorship network connecting the top 25 collaborators of Morgane Rivière. A scholar is included among the top collaborators of Morgane Rivière 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 Morgane Rivière. Morgane Rivière is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

#	Work	Indexed citations
1	The Interspeech Zero Resource Speech Challenge 2021: Spoken language modelling. 2021 ·arXiv (Cornell University) ·Ewan Dunbar, Mathieu Bernard, (unknown), (unknown), (unknown), (unknown), Morgane Rivière, Eugene Kharitonov, Emmanuel Dupoux	1
2	Open Catalyst 2020 (OC20) Dataset and Community Challenges breakdown → 2021 ·ACS Catalysis ·(unknown), Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Rivière, Kevin Tran, Javier Heras‐Domingo, (unknown), Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon M. Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary W. Ulissi	440
3	Correction to “The Open Catalyst 2020 (OC20) Dataset and Community Challenges” 2021 ·ACS Catalysis ·(unknown), Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Rivière, Kevin Tran, Javier Heras‐Domingo, (unknown), Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon M. Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary W. Ulissi	12
4	Inspirational Adversarial Image Generation 2021 ·IEEE Transactions on Image Processing ·Baptiste Rozière, Morgane Rivière, Olivier Teytaud, Jérémy Rapin, Yann LeCun, Camille Couprie	11
5	Towards Unsupervised Learning of Speech Features in the Wild 2021 ·Morgane Rivière, Emmanuel Dupoux	14
6	Text-Free Prosody-Aware Generative Spoken Language Modeling 2021 ·arXiv (Cornell University) ·Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, (unknown), Morgane Rivière, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu	13
7	Data Augmenting Contrastive Learning of Speech Representations in the\n Time Domain 2020 ·arXiv (Cornell University) ·Eugene Kharitonov, Morgane Rivière, Gabriel Synnaeve, Lior Wolf, Pierre-Emmanuel Mazaré, Matthijs Douze, Emmanuel Dupoux	61
8	GDPP: Learning Diverse Generations using Determinantal Point Processes 2019 ·King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) ·(unknown), Camille Couprie, Morgane Rivière, Mohamed Elhoseiny	18

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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