Seminars (hosted by BML)
Anil Yaman (Eindhoven University of Technology), Evolution of biologically inspired learning in artificial neural networks, Aug 22, 2019. (CNAI seminar)
Hyojin Park (University of Birmingham), Neural oscillatory mechanisms in dynamic information representation during natural audio-visual speech perception, Aug 14, 2019. (CNAI seminar)
Joel Z Leibo (Google DeepMind), Autocurricula and the emergence of innovation from social interaction, May 16, 2019. (Bio-IT/BBE/BCE seminar series)
Zeb Kurth-Nelson (Google DeepMind), Distributions from dopamine and factorized replay, MAY 1, 2019. (Bio-IT/BBE/BCE seminar series)
YoungGyun Park (MIT), Toward integrative brain mapping via intact tissue processing and phenotyping techniques, MAY 1, 2019. (BCE seminar series)
Xavier Boix (MIT), Making a science from the computer vision zoo, NOV 15, 2018. (MIR-MSREP seminar series)
Hiroyuki Nakahara (RIKEN), Neural mechanism and computations for social decision-making , OCT 24, 2018. (Bio-IT half-day workshop)
Ales Leonardis (University of Birmingham), Combining vision and physics to explore synergies in scene understanding, AUG 14, 2018. (KI for AI seminar series)
Daeyeol Lee (Yale University), Future of AI: Is the brain a computer?, AUG 1, 2018. (Bio-IT inspiring talk series)
Minjoon Kouh (Drew), Trade-offs in neural computation, June 27, 2018. (Bio-IT inspiring talk series)
Joel Z Leibo (Google DeepMind), The interplay of competition and cooperation in shaping intelligence, MAR 28, 2018. (BBE seminar series)
Choong-Wan Woo (Institute for Basic Science), Pain neuroimaging, DEC 1, 2017. (Computational psychiatry seminar series)
Ben Seymour (University of Cambridge; CINN/ATR/Osaka Univ.), Pain and aversive learning: from computational neuroscience to clinical neuroengineering, NOV 16, 2017. (Computational psychiatry seminar series)
Benedetto Martinos (University College London), Decision uncertainty, OCT 12, 2017. (Computational psychiatry seminar series)
Rongjun Yu (National University of Singapore), The neural basis of decision making under uncertainty, SEP 28, 2017. (BML computational psychiatry seminar series)
Christopher Summerfield (University of Oxford), Neural and computational mechanisms of human decision-making, SEP 13, 2017. (Computational psychiatry seminar series)
Kóczy T. László (Budapest University of Technology and Economics), Fuzzy signature, July 3, 2017.
Erie D. Boorman (University of California at Davis), Computational and representational approaches to associative learning, June 21, 2017. (Computational psychiatry seminar series)
Hyun Kook Lim (Catholic University Saint Vincent Hospital), Alzheimer's disease, Mar 16, 2017.
Seung-Tae Lee (Yonsei University College of Medicine), Next-generation sequencing, Mar 24, 2017.
Minlie Huang (Tsinghua University), New Approaches for Representing Text and Knowledge, NOV 22, 2016.
Mattia Rigotti (IBM TJ Watson), High and low dimensional neural responses for learning and implementing context-dependent behavior, NOV 2, 2016. (Neural computation workshop)
Jinseob Kim (Korea Brain Research Institute), Neural codes of visual perception: single cells and neural circuits in the retina, NOV 2, 2016. (Neural computation workshop)
JeeHang Lee (Yonsei University; University of Bath), Normative decision making, OCT 19, 2016.
Benedetto Martinos (University of Cambridge), The construction of confidence and its role in guiding behavior, OCT 5, 2016. (Computational psychiatry workshop)
Robb Rutledge (University College London), A computational and neural model of momentary subjective well-being, OCT 5, 2016. (Computational psychiatry workshop)
Shinsuke Suzuki (Tohoku University), Value computation in the human brain: its basis and contagious nature, OCT 5, 2016. (Computational psychiatry workshop)
Sukbin Lim (NYU Shanghai), Balanced cortical microcircuitry for working memory and revised NMDA hypothesis, OCT 5, 2016. (Computational psychiatry workshop)
Heyeon Park (Seoul National University Bundang Hospital), Multiple effects of stress on reinforcement learning in a changing environment, SEP 22, 2016.
Yongsek Yoo (Hongik University), A computational model of the medial temporal lobe, AUG 11, 2016.
Demis Hassabis (Google DeepMind - Founder & CEO), Artificial Intelligence and the Future, MAR 11, 2016. (Bio-IT seminar series)
2018 Model-based deep reinforcement learning (PDF flyer)
Date: Mon, FEB 12, 2018 (15:00-18:00)
Venue: #205 (E16-1 YBS Bldg.)
Reinforcement learning + deep learning + Bayesian game theory. This half-day workshop aims to review recent studies about model-based deep reinforcement learning (RL). Model-based RL refers to a class of reinforcement learning algorithms that learn the model of the environment. For example, model-based RL agents are expected to rapidly adapt to the change of the environment structure. It addresses the Bayesian game problem. Imagine you play a Tic Tac Toe, Chess, or GO with the model-based RL agent. It can dominate the game by taking advantage of your game strategy. However, the conventional model-free RL agent (e.g., DQN, SARSA, TD, and etc.) can be fooled by sudden changes of a goal or deliberate changes in your game strategy. This approach offers enormous potential for solving general problems.