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Found 8 papers, 4 papers with code
Upper bounds for integrated information
We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations.
Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms
IIT aims to account for the properties of experience in physical (operational) terms.
System Integrated Information
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience.
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
The proposed scheme, referred to as RL-NCS, aims to boost the performance of signal recovery through an optimal and adaptive distribution of sensing energy among two groups of coefficients of the signal, referred to as the region of interest (ROI) coefficients and non-ROI coefficients.
Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.
Face Image Retrieval With Attribute Manipulation
For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results.
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples.
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?
We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective.
