Natasha Jaques

31 papers receiving 1.1k citations

Peers

Natasha Jaques
Comparison fields: 5 of 116
  • Applied Psychology 169
  • Experimental and Cognitive Psychology 413
  • Cognitive Neuroscience 221
  • Human-Computer Interaction 59
  • Computer Vision and Pattern Recognition 170
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Sharifa Alghowinem United States
Martin Gjoreski Slovenia
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Yekta Said Can Türkiye
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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 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015195
2 2017161
3 2015158
4 201586
5 201772
6 201547
7 201346
8 201739
9
Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
201738
10 202431
11 201531
12 202227
13
Tuning Recurrent Neural Networks with Reinforcement Learning
201624
14 201524
15 202021
16 202413
17
Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning
201611
18 202210
19
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
201910
20 20209

About Natasha Jaques

Natasha Jaques is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Cognitive Neuroscience, Computer Vision and Pattern Recognition and Cardiology and Cardiovascular Medicine, having authored 35 papers that have together received 1.1k indexed citations. Recurring topics across this work include Mental Health Research Topics (5 papers), Emotion and Mood Recognition (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 Speech and dialogue systems (2 papers). The work is most often cited by research in Applied Psychology (169 citations), Experimental and Cognitive Psychology (413 citations), Cognitive Neuroscience (221 citations), Human-Computer Interaction (59 citations) and Computer Vision and Pattern Recognition (170 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, Asma Ghandeharioun, Amy Yu, Andrew J. K. Phillips, Andrew W. McHill, Elizabeth B. Klerman and Charles A. Czeisler. Their work appears in journals such as International Journal of Artificial Intelligence in Education, Frontiers in Behavioral Neuroscience, Proceedings of the National Academy of Sciences, IEEE Transactions on Affective Computing and SLEEP.

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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