Search Results for author: Ann Kennedy

Found 9 papers, 6 papers with code

BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos

1 code implementation CVPR 2023 Jennifer J. Sun, Lili Karashchuk, Amil Dravid, Serim Ryou, Sonia Fereidooni, John Tuthill, Aggelos Katsaggelos, Bingni W. Brunton, Georgia Gkioxari, Ann Kennedy, Yisong Yue, Pietro Perona

In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.

Macroscopic Dynamics of Neural Networks with Heterogeneous Spiking Thresholds

no code implementations7 Sep 2022 Richard Gast, Sara A. Solla, Ann Kennedy

The Izhikevich single neuron model can account for a broad range of different neuron types and spiking patterns, thus rendering it an optimal candidate for a mean-field theoretic treatment of brain dynamics in heterogeneous networks.

Effects of Neural Heterogeneity on Spiking Neural Network Dynamics

no code implementations17 Jun 2022 Richard Gast, Sara A. Solla, Ann Kennedy

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics.

Unsupervised Learning of Neurosymbolic Encoders

1 code implementation28 Jul 2021 Eric Zhan, Jennifer J. Sun, Ann Kennedy, Yisong Yue, Swarat Chaudhuri

We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language.

Program Synthesis Sports Analytics

Interpreting Expert Annotation Differences in Animal Behavior

no code implementations11 Jun 2021 Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue

Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise.

Program Synthesis

Task Programming: Learning Data Efficient Behavior Representations

1 code implementation CVPR 2021 Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona

The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts.

Self-Supervised Learning

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