Search Results for author:
Found 9 papers, 5 papers with code
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations
Taken together, HyperSINDy offers a promising framework for model discovery and uncertainty quantification in real-world systems, integrating sparse equation discovery methods with advances in statistical machine learning and deep generative modeling.
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
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.
Network inference via process motifs for lagged correlation in linear stochastic processes
Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices.
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings
Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain.
Neural-inspired Measurement Observability
The neural encoding by biological sensors of flying insects, which prefilters stimulus data before sending it to the central nervous system in the form of voltage spikes, enables sensing capabilities that are computationally low-cost while also being highly robust to noise.
PySensors: A Python Package for Sparse Sensor Placement
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks.
Numerical differentiation of noisy data: A unifying multi-objective optimization framework
Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control.
Dynamical Systems Signal Processing
Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation
Our proposed PCI method demonstrated consistently strong performance in inferring causal relations for small (2-5 node) and large (10-20 node) networks, with both linear and nonlinear dynamics.
Dynamical Systems Adaptation and Self-Organizing Systems Applications 37M10, 62D20, 62M10
Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
We show results from our approach applied to data collected for 12 human subjects over 7--9 days for each subject.
