imitation learning
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Ann Kennedy
The Kennedy lab is recruiting for multiple funded postdoctoral positions in theoretical and computational neuroscience, following our recent lab move to Scripps Research in San Diego, CA! Ongoing projects in the lab span topics in: reservoir computing with heterogeneous cell types, reinforcement learning/control theory analysis of complex behavior, neuromechanical whole-organism modeling, diffusion models for imitation learning/forecasting of mouse social interactions, joint analysis/modeling of effects of internal states on neural + vocalization + behavior data. With additional NIH and foundation funding for: characterizing progression of behavioral phenotypes in Parkinson’s, modeling cellular/circuit mechanisms underlying internal state-dependent changes in neural population dynamics, characterizing neural correlates of social relationships across species. Projects are flexible and can be tailored to applicants’ research and training goals, and there are abundant opportunities for new collaboration with local experimental groups. San Diego has a fantastic research community and very high quality of life. Our campus is located at the Pacific coast, at the northern edge of UCSD and not far from the Salk Institute. Postdoctoral stipends are well above NIH guidelines and include a relocation bonus, with research professorship positions available for qualified applicants.
Generative models for video games (rescheduled)
Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.
Generative models for video games
Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.
Deep imitation learning for neuromechanical control: realistic walking in an embodied fly
COSYNE 2025
imitation learning coverage
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