This package contains a Python implementation for Quantum Sparse Coding [1]: a quantum-inspired method for recovering a sparse vector given a few noisy linear measurements. [1] Y. Romano, H. Primack, ...

Abstract: An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an ...

Abstract: Sparse coding based domain adaptation methods aim to learn a robust transfer classifier by utilizing the knowledge from source domain and the learned new representation of both domains. Most ...

Sensory neural circuits are thought to efficiently encode incoming signals. Several mathematical theories of neural coding formalize this notion, but it is unclear how these theories relate to each ...

Early cortical sensory areas create internal representations of the sensory world. At the level of individual neurons, this process is reasonably well understood. For instance, in the primary visual ...

Signals from the world (electromagnetic waves, pressure, chemicals etc) are converted to electrical activity in sensory neurons and processed by neuronal networks in the brain. Insects sense smells ...

Experiments for ICML paper DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding, ICML 2018, T. Moreau, L. Oudre, N. Vayatis.

Dwave is describing how they adapted the math to work in a superior way on their adiabatic quantum computer system. This solution indicates how much the smart mathematicians and computer algorithm ...