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: 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 ...

Abstract: Our goal is to find anomalous features in a dataset using the sparse coding concept of dictionary learning. Rather than using the averaged column ℓ2-norm for the dictionary update as is ...

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 ...

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 ...

This repository contains two parts. We theoretically compare EPLISTA with prior works in sparse coding simulation experiments. For vision practice of image denoising, we implement EPCSCNet with ...

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 ...

Sparse coding is a hot area in the field of Deep Learning. Deep learning and sparse coding appears highly promising for increasing the accuracy of image classification and it enables a system to look ...