Linderman, Scott W.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Weinreb, C., Pearl, J. E., Lin, S., Osman, M. A. M., Zhang, L., Annapragada, S., Conlin, E., Hoffmann, R., Makowska, S., Gillis, W. F., et al. (2024) · Nature Methods

Human Pose and Action RecognitionNeurobiology and Insect Physiology Research

Cited by 228

Simplified State Space Layers for Sequence Modeling

Smith, J. T. H., Warrington, A., & Linderman, S. W. (2022) · arXiv

Topic ModelingTime Series Analysis and Forecasting

Cited by 162

Dynamic and reversible remapping of network representations in an unchanging environment

Low, I. I. C., Williams, A. H., Campbell, M. G., Linderman, S. W., & Giocomo, L. M. (2021) · Neuron

Memory and Neural MechanismsSpatial Cognition and Navigation

Cited by 70

Emergence of belief-like representations through reinforcement learning

Hennig, J. A., Romero Pinto, S. A., Yamaguchi, T., Linderman, S. W., Uchida, N., & Gershman, S. J. (2023) · PLOS Computational Biology

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 41

Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models

Narayan, A., Biderman, D., Eyuboglu, S., May, A., Linderman, S., Zou, J., & Re, C. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1