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Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike(More)
Learning from human behaviors in the real world is important for building human-aware intelligent systems such as personalized digital assistants and autonomous humanoid robots. Everyday activities of human life can now be measured through wearable sensors. However, innovations are required to learn these sensory data in an online in-cremental manner over(More)
Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option(More)
Pattern recognition is a major division of machine learning which focuses on learning the patterns and regularities in data. It differs to that of pattern matching where only exact matches are found. However in the field of DNA computing, molecular pattern recognition has not been well established due to the lack of control of molecules in liquid state,(More)
—This paper introduces Glassbot, the agent on glass-type wearable devices with camera and audio sensors. We want to train Glassbot continuously in a wearable device by rapidly adapting deep neural networks from sensor data streams of user behaviors. In this paper, we describe our early works on dataset and online learning algorithms for Glassbot. We also(More)
Various benefits of DNA computing such as programmability, immense data storage capacity and massively parallel processing have led to provide fundamental building blocks to motivated goals of building smart in vivo robots with implications in various applications. However, molecular machine learning has not yet been employed to solve more complex tasks(More)