The main goal of this assignment was to grasp an understanding of how divide and conquer algorithms work while experimenting with the time and space efficiency between divide & conquer, and ...

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 ...

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

With AlphaTensor, DeepMind Technologies has presented an AI system that is supposed to independently find novel, efficient and provably correct algorithms for complex mathematical tasks. AlphaTensor ...

Researchers at MIT's Computer Science & Artificial Intelligence Lab (CSAIL) have open-sourced Multiply-ADDitioN-lESS (MADDNESS), an algorithm that speeds up machine learning using approximate matrix ...

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...

The new version of AlphaZero discovered a faster way to do matrix multiplication, a core problem in computing that affects thousands of everyday computer tasks. DeepMind has used its board-game ...

Abstract: General Matrix-Matrix Multiplication (GEMM) stands as the most ubiquitous operation in machine learning applications. However, performing GEMM within Fully Homomorphic Encryption (FHE) is ...

Abstract: In this paper, a high-order multiplication perturbation-based transition matrix method (TM-HOMP) is proposed to address the strongly terminal-constrained optimal control problem (OCP) in ...