Daniel Kramer — accepted AI-conference papers

2022

Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

Daniel Kramer, Philine L Bommer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz

ICML 2022spotlight

Empirically observed time series in physics, biology, or medicine, are commonly generated by some underlying dynamical system (DS) which is the target of scientific interest. There is an increasing interest to harvest machine learning methods to reconstruct this latent DS in a data-driven, unsupervi…

Cited by 26 Source ↗PDF ↗code ↗Scholar ↗

2019

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

Benyamin Allahgholizadeh Haghi, Spencer Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford, Daniel Kramer +4

NeurIPS 2019poster

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗