Modern language models are trained on data with extremely uneven token distributions. A small number of words appear in almost every sentence, while many rare but meaningful tokens occur only ...

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 ...

Stochastic Gradient Descent, a cornerstone of modern machine learning, frequently powers the algorithms that underpin large-scale data analysis, yet its behaviour in complex, high-dimensional ...

Abstract: This letter presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum ...

Like a feather in the wind or the global stock market, all of us are subject to stochastic drift. Originating in advanced statistics, the term refers to the way in which randomness works upon a system ...

ABSTRACT: The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For ...

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, ...

A new publication in Opto-Electronic Advances discusses efficient stochastic parallel gradient descent training for on-chip optical processors. With the explosive growth of the global data volume, ...

Linguist and lexicographer Ben Zimmer analyzes the origins of words in the news. Read previous columns here. On Jan. 5, a few hundred linguists gathered in a ballroom in midtown Manhattan to mull ...