Seminars (hosted by BML)
Dongqi Han (Microsoft Research Asia)
Efficient prior and flexible posterior – a Bayesian perspective on behavior, Oct 28, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Yansen Wang (Microsoft Research Asia)
Towards extending the boundary of non-invasive BCI, Oct 28, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Marcelo G Mattar (NYU)
Modeling and measuring human planning and hippocampal replay, Oct. 16, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Mattia Rigotti (IBM AI research)
A distributional approach to human preferences for LLM alignment, Oct. 2, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Kevin Max (Univ of Bern)
Learning, forward and backward, September 20, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Daniel McNamee (Champalimaud)
The rational case against generative world models, July 11, 2024.
(2024 KAI-X Seminar Series on Neuromorphic Computing and Natural Intelligence)Miran Lee (Microsoft Research Asia)
The vision and approaches of Microsoft Research Asia in brain and neuroscience, Nov 22, 2023.
(MSRA x KAIST BCS workshop)Dongsheng Li (Microsoft Research Asia)
When machine learning meets the brain, Nov 22, 2023.
(MSRA x KAIST BCS workshop)Dongqi Han (Microsoft Research Asia)
A variational Bayesian perspective of habits and goals: insights for psychology and AI, Nov 22, 2023.
(MSRA x KAIST BCS workshop)Doo-sup Choi (Mayo Clinic)
Computational Neuroscience and Behavior Disorders: Neural Circuits and Artificial Intelligence in Predicting Behaviors, NOV 8, 2023. (BCS colloquium)Brenden Lake (NYU)
Machine learning through the eyes and ears of a child, NOV 4, 2023. (BCS Symposium)Catherine A. Hartley (NYU)
Neural and cognitive mechanisms of developmental change in goal-directed behavior, NOV 4, 2023. (BCS Symposium)Takuya Ito (IBM Research)
Multitask and compositional capacities in human brains and machines, NOV 3, 2023. (BCS Symposium)Joel Z Leibo (Google DeepMind)
A theory of appropriateness with applications to generative artificial intelligence, NOV 3, 2023. (BCS Symposium)Taro Toyoizumi (RIKEN)
Information theoretical approaches to model synaptic plasticity, OCT 4, 2023. (BCS colloquium)Jonathan Rubin (University of Pittsburgh)
Cortico-basal ganglia-thalamic control ensembles shift decision policies to maximize reward rate during learning, SEP 27, 2023. (BCS colloquium)Aurelio Cortese (ATR Computational Neuroscience Labs)
Confidence, abstractions and reinforcement learning in humans and machines, SEP 6, 2023. (BCS Colloquium)Andrea Tacchetti (DeepMind)
The good shepherd: Machine learning for mechanism design, Oct 5, 2022. (BBE/BCE seminar series + CNAI workshop)Doo-Sup Choi (Mayo Clinic)
Neuroscience of breaking the bad habits: molecular and computational approaches focusing on striatum-pallidal circuits, OCT 27, 2021. (BBE seminar series)Bruno Averbeck (NIH/NIMH)
Computational mechanisms and neural systems underlying reinforcement learning, OCT 13, 2021. (BBE/BCE seminar series)Ben Seymour (Oxford)
Looking for Pain in the Brain, April 14, 2021. (BBE/BCE seminar series)Andrew Saxe (Oxford)
Dynamics of learning contextual, controlled and abstract representations in deep neural networks, Nov 5, 2020. (KI AI + CNAI workshop)Andrea Tacchetti (DeepMind)
Learning in multi-agent systems, Nov 5 2020. (KI AI + CNAI workshop)Joel Z Leibo (DeepMind)
Multi-agent reinforcement learning, Dec 3, 2019. (KI AI seminar series)Gabriel Kreiman (Harvard Medical School)
Peeking inside the brain to develop the next generation of AI, OCT, 30, 2019. (KI AI + CNAI workshop).Rahul Bhui (Harvard Univeristy)
Efficient coding in economic judgment, OCT, 30, 2019. (KI AI + CNAI workshop).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 (DeepMind)
Autocurricula and the emergence of innovation from social interaction, May 16, 2019. (Bio-IT/BBE/BCE seminar series)Zeb Kurth-Nelson (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)Keun-Ah Cheon (Yonsei University College of Medicine)
Neural basis of aberrant social communication in autism spectrum disorder, May 9, 2018. (BBE/BCE seminar series)Yoonsuck Choe (Texas A&M) (BBE/BCE seminar series)
Overcoming limitations of deep learning, April 25, 2018. (BBE/BCE seminar 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 (Oxford/DeepMind)
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.