Recent advances in machine learning have led to increased interest solving visual computing problems using methods that employ coordinate-based neural networks. These methods, which we call fields, parameterize physical properties of scenes or objects across space and time. They seen widespread success such as 3D shape image synthesis, animation human bodies, reconstruction, pose estimation. Ra...

Analog electronic and optical computing exhibit tremendous advantages over digital for accelerating deep learning when operations are executed at low precision. Although architectures support programmable precision to increase efficiency, analog today only a single, static In this work, we characterize the relationship between effective number of bits (ENOB) processors, which is limited by nois...

As new areas of neural computing are trying to make at least one step beyond the definition of digital computing, the neural networks field, was developed around the idea of creating models of real neural systems. The key point is based on learning rather than programming. We designed the model we present in this paper, based on the the needs introduced by new trends in neural computing. We cre...

Since Shor (1994) proposed a way of factorizing large integers in polynomial time by using a quantum computing algorithm, the study of quantum information science, including quantum communication, quantum cryptography, quantum computer and so on, has been intensified (Nielsen & Chuang,2000). Shor’s proposal has not only proved itself to be a milestone in quantum computing, but also created a no...

This paper shows the analysis and design of feedforward neural networks using the coordinate-free system of Clifford or geometric algebra. It is shown that real-, complex-, and quaternion-valued neural networks are simply particular cases of the geometric algebra multidimensional neural networks and that some of them can also be generated using support multivector machines (SMVMs). Particularly...

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is...