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, ...

As transistors start to approach fundamental physical limits and Moore's law slows down, new devices and architectures are needed to enable continued computing performance gains (Theis and Solomon, ...

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 ...

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: Non-Intrusive Load Monitoring (NILM), also referred to as Energy Disaggregation, is a family of methods that predict the appliance consumption signals of a building using only its main ...

The use of wearable technology for monitoring a person’s health status is becoming increasingly more popular. Unfortunately, this technology typically suffers from low-quality measurement data, making ...

Re-implementation of the paper Fast and Flexible Convolutional Sparse Coding (of 3 authors Felix Heide, Wolfgang Heidrich, Gordon Wetzstein) in Python https://www.cv ...

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 ...