Natasha Jaques

34 papers receiving 1.3k citations

Peers

Natasha Jaques
Comparison fields: 5 of 122
  • Applied Psychology 192
  • Experimental and Cognitive Psychology 496
  • Human-Computer Interaction 112
  • Cognitive Neuroscience 271
  • Computer Science Applications 79
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Citations per year

Countries citing papers authored by Natasha Jaques

Since Specialization
Citations

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

Fields of papers citing papers by Natasha Jaques

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Natasha Jaques, 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 Natasha Jaques Line = papers co-authored together Natasha Jaques links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015204
2 2017178
3 2015176
4 2014117
5 201595
6 201782
7 201357
8 201549
9
Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
201741
10 201741
11 201536
12 202431
13 202231
14 201526
15
Tuning Recurrent Neural Networks with Reinforcement Learning
201625
16 201623
17 202021
18 202414
19
Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning
201612
20 202210

About Natasha Jaques

Natasha Jaques is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Cognitive Neuroscience, Computer Vision and Pattern Recognition and Social Psychology, having authored 39 papers that have together received 1.3k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (6 papers), Mental Health Research Topics (5 papers), Music Technology and Sound Studies (4 papers), Music and Audio Processing (4 papers), EEG and Brain-Computer Interfaces (4 papers), Digital Mental Health Interventions (3 papers), Reinforcement Learning in Robotics (3 papers) and Emotions and Moral Behavior (2 papers). The work is most often cited by research in Applied Psychology (192 citations), Experimental and Cognitive Psychology (496 citations), Human-Computer Interaction (112 citations), Cognitive Neuroscience (271 citations) and Computer Science Applications (79 citations). Natasha Jaques has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Rosalind W. Picard, Akane Sano, Sara Taylor, Szymon Fedor, Cristina Conati, Roger Azevedo, Jason M. Harley, Andrew W. McHill, Andrew J. K. Phillips and Elizabeth B. Klerman. Their work appears in journals such as IEEE Transactions on Affective Computing, SLEEP, International Journal of Artificial Intelligence in Education, Proceedings of the National Academy of Sciences and Frontiers in Behavioral Neuroscience.

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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