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Found 13 papers, 5 papers with code
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging.
Distance preservation in state-space methods for detecting causal interactions in dynamical systems
We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems.
Learning Internal Representations of 3D Transformations from 2D Projected Inputs
When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images.
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control
We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models.
Learning Identity-Preserving Transformations on Data Manifolds
However, these approaches are limited because they require transformation labels when training their models and they lack a method for determining which regions of the manifold are appropriate for applying each specific operator.
Feedback Coding for Active Learning
The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise.
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Unfortunately, current proposals for sparse coding in the compressed space require a centralized compression process (i. e., dense random matrix) that is biologically unrealistic due to local wiring constraints observed in neural circuits.
Generative causal explanations of black-box classifiers
Our objective function encourages both the generative model to faithfully represent the data distribution and the latent factors to have a large causal influence on the classifier output.
Representing Closed Transformation Paths in Encoded Network Latent Space
Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space.
Active Ordinal Querying for Tuplewise Similarity Learning
Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans.
Transfer Learning on Manifolds via Learned Transport Operators
The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data.
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks
In most existing analyses, the short-term memory (STM) capacity results conclude that the ESN network size must scale linearly with the input size for unstructured inputs.
Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization
Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.
