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Publication search results
found 31 matches
- 2019
- Akinori Abe:
Interpretable AI as Curation. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - David Agogo, Leona Chandra Kruse:
Open Affect-Responsive Systems: Toward Personalized AI to Beat Back the Waves of Technostress. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Anas Al-Tirawi, Robert G. Reynolds:
Maintaining Knowledge Distribution System's Sustainability Using Common Value Auctions. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Christina Alexandris:
Evaluating Cognitive Bias in Two-Party and Multi-Party Spoken Interactions. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Oliver Bendel:
Are Robot Tax, Basic Income or Basic Property Solutions to the Social Problems of Automation? AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Umang Bhatt, Brian Davis, José M. F. Moura:
Diagnostic Model Explanations: A Medical Narrative. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Amy Wenxuan Ding:
Treat Everyone Fairly: A Model of Unbiased and Explainable Algorithmic Decision Making. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Sidharth Goel:
DeepVariant: Deep Learning for Genomic Variant Calling. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Saveli Goldberg, Boris Galitsky, Ben Weisburd:
Framework for interaction between expert users and Machine Learning Systems. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Teruaki Hayashi, Yukio Ohsawa:
Context-based Network Analysis of Structured Knowledge for Data Utilization. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Takashi Kido, Keiki Takadama:
The Challenges for Interpretable AI for Well-being. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Pang Wei Koh:
Identifying and exploiting influential training examples. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Takeshi Konno, Hiroaki Kingetsu, Daisuke Fukuda, Toshihiro Sonoda:
Fall Risk Detection for the Elderly using Contactless Sensors. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Xuehui Leng, Masanao Ochi, Takeshi Sakaki, Junichiro Mori, Ichiro Sakata:
A Cross-lingual Analysis on Culinary Perceptions to Understand the Cross-cultural Difference. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Iko Nakari, Yusuke Tajima, Akari Tobaru, Keiki Takadama:
WAKE Detection During Sleep using Random Forest for Apnea Syndrome Patients. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Miwa Nishinaka:
Future Prototyping Methodology: What is a well-being for the Future Society and how AI handles "appropriateness. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Miwa Nishinaka, Yusuke Kishita, Hisashi Masuda, Kunio Shirahada:
Concept of Future Prototyping Methodology to Enhance Value Creation within Future Contexts. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Nobuyuki Oishi, Masayuki Numao:
Measuring Functional Independence of an Aged Person with a Combination of Machine Learning and Logical Reasoning. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Judea Pearl:
What is Causal Inference? AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Peter Pirolli:
Interpretable AI for Well-Being Using Mobile Health. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Sadeq Rahimi:
Extended Mind, Embedded AI, and "the Barrier of Meaning". AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Madeleine Schneider, Robert Thomsons:
What Makes a Good Diagnosis: An Algorithm to Detect Biased Training Data. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Morteza Shahrezaye, Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich:
Estimating the Political Orientation of Twitter Users in Homophilic Networks. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Avanti Shrikumar:
Suggested Best Practices for Interpreting Deep Learning Models via Input-Level Importance Scores. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Keiki Takadama:
What Makes It Difficult To Apply AI Into Well-being and Its Solution: An Example of Sleep Apnea Syndrome. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Ryo Takano, Sho Kajihara, Satoshi Hasegawa, Eiki Kitajima, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto:
Toward Good Circadian Rhythm through an valuate of Stress Condition. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Ryo Takano, Akari Tobaru, Iko Nakari, Keiki Takadama:
Sleep Stage Estimation Through Mattress Sensor. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Akari Tobaru, Yusuke Tajima, Keiki Takadama:
Sleep Stage Estimation using Heart Rate Variability divided by Sleep Cycle with Relative Evaluation. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Changeun Yang, Pujana Paliyawan, Ruck Thawonmas, Tomohiro Harada:
TGIF!: Selecting the most healing TNT by optical flow. AAAI Spring Symposium: Interpretable AI for Well-being 2019 - Yuichi Yoda, Kosuke Mizokushi, Seiichiro Honjo:
A Study of Basis on AI-based Information Systems: The Case of Shogi AI System "Ponanza". AAAI Spring Symposium: Interpretable AI for Well-being 2019
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