Uploaded May 2026 | Updated September 2026, 2 weeks ago
Title: Machine Learning with Less Data
Speaker: Brian Kenji Iwana, Kyushu University
Abstract: Recently, the field of artificial intelligence (AI) has witnessed remarkable advancements and has worked its way into daily life. However, modern AI systems are built on artificial neural networks, which still have many unresolved challenges. One of these challenges is the reliance on massive amounts of data. In many real-world applications, such large annotated datasets are difficult or expensive to obtain, which can prevent models from being trained effectively and limit their practical deployment. In this talk, I will give an overview of current practices for overcoming limited data problems and introduce new solutions.
Profile: Brian Kenji Iwana is an Associate Professor at Kyushu University in the Faculty of Information Science and Electrical Engineering. His research includes pattern recognition and machine learning. Specifically, he works with artificial neural networks, time series recognition, image recognition, and natural language processing. He received his PhD from Kyushu University and his Bachelors degree at the University of California, Irvine. Prior to that, he worked at the National Aeronautics and Space Administration in California.
Find out more about the TSVP on the program website: oist.jp/visiting-program
#MachineLearning #ArtificialIntelligence #lessdata #OIST #OIST-TSVP #Theoretical #Science #VisitingProgram #Okinawa #TSVP
Title: Machine Learning with Less Data
Speaker: Brian Kenji Iwana, Kyushu University
Abstract: Recently, the field of artificial intelligence (AI) has witnessed remarkable advancements and has worked its way into daily life. However, modern AI systems are built on artificial neural networks, which still have many unresolved challenges. One of these challenges is the reliance on massive amounts of data. In many real-world applications, such large annotated datasets are difficult or expensive to obtain, which can prevent models from being trained effectively and limit their practical deployment. In this talk, I will give an overview of current practices for overcoming limited data problems and introduce new solutions.
Profile: Brian Kenji Iwana is an Associate Professor at Kyushu University in the Faculty of Information Science and Electrical Engineering. His research includes pattern recognition and machine learning. Specifically, he works with artificial neural networks, time series recognition, image recognition, and natural language processing. He received his PhD from Kyushu University and his Bachelors degree at the University of California, Irvine. Prior to that, he worked at the National Aeronautics and Space Administration in California.
Find out more about the TSVP on the program website: oist.jp/visiting-program
#MachineLearning #ArtificialIntelligence #lessdata #OIST #OIST-TSVP #Theoretical #Science #VisitingProgram #Okinawa #TSVP
