L
Lisa Anne Hendricks
Researcher at University of California, Berkeley
Publications - 53
Citations - 14176
Lisa Anne Hendricks is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Closed captioning & Computer science. The author has an hindex of 26, co-authored 44 publications receiving 10932 citations. Previous affiliations of Lisa Anne Hendricks include Adobe Systems & Lawrence Berkeley National Laboratory.
Papers
More filters
Proceedings ArticleDOI
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue,Lisa Anne Hendricks,Sergio Guadarrama,Marcus Rohrbach,Subhashini Venugopalan,Trevor Darrell,Kate Saenko +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
Posted Content
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue,Lisa Anne Hendricks,Marcus Rohrbach,Subhashini Venugopalan,Sergio Guadarrama,Kate Saenko,Trevor Darrell +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
Journal ArticleDOI
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue,Lisa Anne Hendricks,Marcus Rohrbach,Subhashini Venugopalan,Sergio Guadarrama,Kate Saenko,Trevor Darrell +6 more
TL;DR: In this article, a class of recurrent convolutional architectures was proposed for large-scale visual understanding tasks, and demonstrated the value of these models for activity recognition, image captioning, and video description.
Book ChapterDOI
Generating Visual Explanations
Lisa Anne Hendricks,Zeynep Akata,Marcus Rohrbach,Marcus Rohrbach,Jeff Donahue,Bernt Schiele,Trevor Darrell +6 more
TL;DR: A new model is proposed that focuses on the discriminating properties of the visible object, jointly predicts a class label, and explains why the predicted label is appropriate for the image, and generates sentences that realize a global sentence property, such as class specificity.
Journal ArticleDOI
Training Compute-Optimal Large Language Models
Jordan Hoffmann,Sebastian Borgeaud,Arthur Mensch,Elena Buchatskaya,Trevor Cai,Eliza Rutherford,Diego de Las Casas,Lisa Anne Hendricks,Johannes Welbl,Aidan Clark,Tom Hennigan,Eric Noland,Katie Millican,George van den Driessche,Bogdan Damoc,Aurelia Guy,Simon Osindero,Karen Simonyan,Erich Elsen,Jack W. Rae,Oriol Vinyals,Laurent Sifre +21 more
TL;DR: This paper trains a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4 × more more data, and reaches a state-of-the-art average accuracy on the MMLU benchmark.