M
Margaret Mitchell
Researcher at Google
Publications - 109
Citations - 18187
Margaret Mitchell is an academic researcher from Google. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 42, co-authored 94 publications receiving 13094 citations. Previous affiliations of Margaret Mitchell include University of Aberdeen & Johns Hopkins University.
Papers
More filters
Proceedings ArticleDOI
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Leandro von Werra,Lewis C. Tunstall,Abhishek Thakur,Alexandra Luccioni,Tristan Thrush,Aleksandra Piktus,Felix Marty,Nazneen Fatema Rajani,Victor Mustar,Helen Ngo,Omar Sanseviero,Mario vSavsko,Albert Villanova,Quentin Lhoest,Julien Chaumond,Margaret Mitchell,Alexander M. Rush,Thomas Wolf,Douwe Kiela +18 more
TL;DR: Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models, and Evaluation on the Hub is a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button.
Posted Content
Learning Visual Classifiers using Human-centric Annotations.
TL;DR: This paper proposes an algorithm to decouple the human reporting bias from the correct visually grounded labels for learning image classifiers, and provides results that are highly interpretable for reporting “what’s in the image” versus “ what”s worth saying.
Posted Content
Measuring Machine Intelligence Through Visual Question Answering
C. Lawrence Zitnick,Aishwarya Agrawal,Stanislaw Antol,Margaret Mitchell,Dhruv Batra,Devi Parikh +5 more
TL;DR: A case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence, and an alternative and more promising task that tests a machine’s ability to reason about language and vision.
Patent
Sentiment-based recommendations as a function of grounding factors associated with a user
Bill Dolan,Margaret Mitchell,Jay Banerjee,Pallavi Choudhury,Susan Hendrich,Rebecca Mason,Ron Owens,Mouni Reddy,Yaxiao Song,Kristina Toutanova,Liang Xu,Xuetao Yin +11 more
TL;DR: In this article, the Facet Recommender applies a machine-learned facet model and an optional sentiment model to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with them.
Proceedings Article
Discourse-Based Modeling for AAC
Margaret Mitchell,Richard Sproat +1 more
TL;DR: A method for an AAC system to predict a whole response given features of the previous utterance from the interlocutor, which uses a large corpus of scripted dialogs, and predicts features that the response should have, using an entropy-based measure.