Matrix factorization algorithms help track neuronal activity They then excited the beads using blue laser light and collected the resulting fluorescence speckles using first a microscope objective and ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

A Hong Kong-based Matrix AI Network is developing a prototype of a new hybrid PoS/PoW consensus algorithm. This update was shared with Cointelegraph by Owen Tao, the company’s CEO.

The 'algorithm for calculating the matrix product' that AlphaTensor worked on this time is used in various fields related to daily life, such as image processing, game graphics processing, weather ...

High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers ...

A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...

This approach enables block encoding of any sparse matrix with a clear gate-level construction, significantly reducing circuit complexity and control overhead, bringing quantum algorithms closer to ...

When the equation AXB = C is consistent over the generalized reflexive (or anti-reflexive) matrix X, for any generalized reflexive (or anti-reflexive) initial iterative matrix X₁, the generalized ...

The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array of useful applications in engineering, machine learning, ...