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 ...

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: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...

If it feels like social platforms suddenly “get” you more than they used to, you’re not imagining it! In 2026, feeds aren’t only reacting to what you click anymore. They’re predicting what you ...

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 ...

Gradient descent is like hiking downhill with your eyes closed, following the slope until you hit the bottom (or at least a nice flat spot to rest). Technically, it is a method to minimize an ...

Performing gradient descent for calculating slope and intercept of linear regression using sum square residual or mean square error loss function. A "from-scratch" 2 ...

"### Gradient descent is an algorithm used to the find local minima of any differentiable function. More formally, given a differentiable function $f(x),$ the ...