Scientists are rethinking what cattle are capable of after an Austrian cow named Veronika was found to use tools with impressive skill. The discovery, reported by researchers in Vienna, suggests cows may have far greater cognitive abilities than previously assumed. Veronika, a cow living in a mountain village in the Austrian countryside, has spent years perfecting the art of scratching herself using sticks, rakes, and brooms. Word of her behaviour eventually reached animal intelligence specialists in Vienna, who found Veronika used both ends of the same object for different tasks. If it were her back or another tough area that warranted a good scratch, she would use the bristle end of a broom.

The evergreen trees give kangaroos, bison, lions, and more extra shelter and fun. Capybaras use donated Christmas trees as wind breaks to protect their habitats. Breakthroughs, discoveries, and DIY tips sent every weekday. The presents are unwrapped, the cookies are crumbs, and that real Christmas tree will become a fire hazard soon enough. Most of us haul it out to the curb for our local sanitation departments to take care of, but some lucky trees make it into the paws of animals living in zoos.

Stochastic Gradient Descent (SGD) is arguably the most important single algorithm in modern machine learning. Although SGD with unbiased gradient estimators has been studied extensively over at least half a century, SGD variants relying on biased estimators are rare. Nevertheless, there has been an increased interest in this topic in recent years. However, existing literature on SGD with biased estimators lacks coherence since each new paper relies on a different set of assumptions, without any clear understanding of how they are connected, which may lead to confusion. We address this gap by establishing connections among the existing assumptions, and presenting a comprehensive map of the underlying relationships. Additionally, we introduce a new set of assumptions that is provably weaker than all previous assumptions, and use it to present a thorough analysis of BiasedSGD in both convex and non-convex settings, offering advantages over previous results. We also provide examples where biased estimators outperform their unbiased counterparts or where unbiased versions are simply not available. Finally, we demonstrate the effectiveness of our framework through experimental results that validate our theoretical findings.

There are so many models in the literature that it is difficult for practitioners to decide which combinations are likely to be effective for a new task. This paper attempts to address this question by capturing relationships among checkpoints published on the web.