Laboratory for Brain and Machine Intelligence

Laboratory for Brain and Machine Intelligence @ KAIST

Laboratory for brain and machine intelligence, KAIST


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)


Lab workshops

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.