David M. Zoltowski

Affiliations:
  • Princeton University, Neuroscience Institute, NJ, USA
  • Michigan State University, Department of Electrical and Computer Engineering, East Lansing, MI, USA


According to our database1, David M. Zoltowski authored at least 12 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.
CoRR, 2024

Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2021
Slice Sampling Reparameterization Gradients.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020
A general recurrent state space framework for modeling neural dynamics during decision-making.
Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations.
CoRR, 2019

2018
Scaling the Poisson GLM to massive neural datasets through polynomial approximations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG.
IEEE Trans. Biomed. Eng., 2017

2014
Low-rank tensor decomposition based dynamic network tracking.
Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing, 2014

A graph theoretic approach to dynamic functional connectivity tracking and network state identification.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

Sparsity-promoting optimal control of spatially-invariant systems.
Proceedings of the American Control Conference, 2014


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