Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Asma Ghandeharioun"'
Autor:
Paola Pedrelli, Szymon Fedor, Asma Ghandeharioun, Esther Howe, Dawn F. Ionescu, Darian Bhathena, Lauren B. Fisher, Cristina Cusin, Maren Nyer, Albert Yeung, Lisa Sangermano, David Mischoulon, Johnathan E. Alpert, Rosalind W. Picard
Publikováno v:
Frontiers in Psychiatry, Vol 11 (2020)
Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performa
Externí odkaz:
https://doaj.org/article/64f1c60c7d2e4769a70040d5e4069798
Autor:
Roohallah Alizadehsani, Jafar Habibi, Behdad Bahadorian, Hoda Mashayekhi, Asma Ghandeharioun, Reihane Boghrati, Zahra Alizadeh Sani
Publikováno v:
Journal of Medical Signals and Sensors, Vol 2, Iss 3, Pp 153-159 (2012)
Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis
Externí odkaz:
https://doaj.org/article/e28cd7f18711451da18ebb8c64e86298
Publikováno v:
AAAI
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the gene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc57ee2633280ab869096ce0aeb966c4
https://doi.org/10.1609/aaai.v34i05.6400
https://doi.org/10.1609/aaai.v34i05.6400
Autor:
Szymon Fedor, Esther Howe, Lisa Sangermano, Maren Nyer, Albert Yeung, Darian Bhathena, Johnathan E. Alpert, Cristina Cusin, Asma Ghandeharioun, Rosalind W. Picard, David Mischoulon, Paola Pedrelli, Dawn F. Ionescu, Lauren B. Fisher
Publikováno v:
Frontiers in Psychiatry, Vol 11 (2020)
Frontiers in Psychiatry
Frontiers in Psychiatry
Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df88084b46dee640b167b52dfd8f3d55
https://www.frontiersin.org/articles/10.3389/fpsyt.2020.584711/full
https://www.frontiersin.org/articles/10.3389/fpsyt.2020.584711/full
Autor:
Noah Jones, Natasha Jaques, Craig Ferguson, Asma Ghandeharioun, Shixiang Shane Gu, Judy Hanwen Shen, Agata Lapedriza, Rosalind W. Picard
Publikováno v:
EMNLP (1)
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c16c0690e9c7011b5bf80749d791444
http://arxiv.org/abs/2010.05848
http://arxiv.org/abs/2010.05848
Autor:
Rosalind W. Picard, Jinmo Lee, Javier Hernandez, Neska El Haouij, Asma Ghandeharioun, Sebastian Zepf
Publikováno v:
UMAP
Driving can occupy a considerable part of our daily lives and is often associated with high levels of stress. Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b701bd948d52f916dddbabe6e3981bcc
https://doi.org/10.1145/3340631.3394854
https://doi.org/10.1145/3340631.3394854
Publikováno v:
ACII Workshops
Machine learning to infer suicide risk and urgency is applied to a dataset of Reddit users in which the risk and urgency labels were derived from crowdsource consensus. We present the results of machine learning models based on transfer learning from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87a358d1fb3dcc29c60b12381d54c07c
https://doi.org/10.1109/aciiw.2019.8925077
https://doi.org/10.1109/aciiw.2019.8925077
Publikováno v:
arXiv
ACII
ACII
We engineered an interactive music system that influences a user's breathing rate to induce a relaxation response. This system generates ambient music containing periodic shifts in loudness that are determined by the user's own breathing patterns. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f317e5eab41648e57f2e072478826599
http://arxiv.org/abs/1907.08844
http://arxiv.org/abs/1907.08844
Publikováno v:
arXiv
ICCV Workshops
ICCV Workshops
Supporting model interpretability for complex phenomena where annotators can legitimately disagree, such as emotion recognition, is a challenging machine learning task. In this work, we show that explicitly quantifying the uncertainty in such setting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::197e7cc34c22140a5b86e8d4105a2802
Publikováno v:
ACII
A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health/wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b5f10b11ac245ac3b0187fbd9567df9