Gradient descent has a fundamental limitation: on most real-world loss surfaces, it is inefficient. When the surface has uneven curvature—steep in one direction and flat in another, which is common in ...

If you are someone who likes all sorts of adventures, like looking for past explorers while you find yourself stranded on a cold deserted island then Deep Descent is just the game for you. Here, your ...

As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...

Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...

Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...

Abstract: Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several ...

1 Department of Mathematics, University of Ndjamena, Ndjamena, Tchad. 2 Department of Mathematics and Computer Science, University of Cheikh. A. Diop, Dakar, Senegal. In the evolving landscape of ...

ABSTRACT: The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, ...