Search Results for author: Suzanne Petryk

Found 5 papers, 2 papers with code

Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly

no code implementations28 Apr 2022 Spencer Whitehead, Suzanne Petryk, Vedaad Shakib, Joseph Gonzalez, Trevor Darrell, Anna Rohrbach, Marcus Rohrbach

This new problem formulation, metric, and analysis for VQA provide the groundwork for building effective and reliable VQA models that have the self-awareness to abstain if and only if they don't know the answer.

Question Answering Visual Question Answering +1

On Guiding Visual Attention with Language Specification

no code implementations17 Feb 2022 Suzanne Petryk, Lisa Dunlap, Keyan Nasseri, Joseph Gonzalez, Trevor Darrell, Anna Rohrbach

To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model.

Classification Fairness

NBDT: Neural-Backed Decision Tree

no code implementations ICLR 2021 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

NBDT: Neural-Backed Decision Trees

2 code implementations1 Apr 2020 Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.

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