Search Results for author: Angie Boggust

Found 6 papers, 5 papers with code

Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods

no code implementations7 Jun 2022 Angie Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan

Saliency methods calculate how important each input feature is to a machine learning model's prediction, and are commonly used to understand model reasoning.

Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior

1 code implementation20 Jul 2021 Angie Boggust, Benjamin Hoover, Arvind Satyanarayan, Hendrik Strobelt

Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior.

Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos

1 code implementation ICCV 2021 Brian Chen, Andrew Rouditchenko, Kevin Duarte, Hilde Kuehne, Samuel Thomas, Angie Boggust, Rameswar Panda, Brian Kingsbury, Rogerio Feris, David Harwath, James Glass, Michael Picheny, Shih-Fu Chang

Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities.

Contrastive Learning Self-Supervised Learning +3

Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples

1 code implementation10 Dec 2019 Angie Boggust, Brandon Carter, Arvind Satyanarayan

Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics.

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