Timnit Gebru

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This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative parts. To this end, we propose an object representation that detects important parts and describes(More)
Targeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and(More)
The United States spends more than $1B each year on initiatives such as the Amer-ican Community Survey (ACS), a labor-intensive door-to-door study that measures demographic factors 1. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous(More)
Meeting uncertain supply conditions with adequate demandside measures is becoming increasingly central to the day-to-day operations of energy utility companies, as variability is one of the main drivers of cost in operating the grid. In this paper we provide the first characterization of demand variability and the factors influencing it. We propose that(More)
We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts. In animals, there is a direct link between taxonomy and visual similarity: e.g. a collie (type of dog) looks more similar to other collies (e.g. smooth collie) than a greyhound (another type of dog). However, in synthetic(More)
Fine-grained recognition refers to the task in computer vision of automatically differentiating similar object categories from one another, e.g. species of birds, types of cars, breeds of dogs, or varieties of aircraft. Since this is a task that the majority of humans are untrained in, any progress has the promise of augmenting human vision. Applications(More)
While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild. These fully supervised models need additional annotated images to classify objects in every new scenario, a task that is infeasible. However, sources such as e-commerce websites and field guides provide(More)
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