Ruth Fong - RankMe

41 papers A* 8Journal 21Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Characterizing the Relationship Between Generative AI, Student Behavior, and Learning Outcomes in Upper-Level CS Education: A Case Study in an Undergraduate Machine Learning Course. SIGCSE (2) Anha Khan, Romina Mahinpei, Maryam Hedayati, Victoria Dean, Ruth Fong
2025	—	`conf`	Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations. CHI Extended Abstracts Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat, Rohan Jinturkar, Olga Russakovsky, Ruth Fong, Parastoo Abtahi
2025	J	`jnl`	Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations. CoRR Indu Panigrahi, Sunnie S. Y. Kim, Amna Liaqat, Rohan Jinturkar, Olga Russakovsky, Ruth Fong, Parastoo Abtahi
2024	—	`conf`	Interactive Visual Feature Search. XAI4CV Devon Ulrich, Ruth Fong
2023	A*	`conf`	"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. CHI Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
2023	A*	`conf`	Gender Artifacts in Visual Datasets. ICCV Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
2023	—	`conf`	Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application. FAccT Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
2023	J	`jnl`	Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application. CoRR Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
2023	—	`conf`	Improving Data-Efficient Fossil Segmentation via Model Editing. CVPR Workshops Indu Panigrahi, Ryan Manzuk, Adam Maloof, Ruth Fong
2023	A*	`conf`	Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability. CVPR Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
2023	J	`jnl`	UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs. CoRR Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
2022	J	`jnl`	"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. CoRR Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
2022	J	`jnl`	ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features. CoRR Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong, Olga Russakovsky
2022	J	`jnl`	Gender Artifacts in Visual Datasets. CoRR Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
2022	—	`conf`	HIVE: Evaluating the Human Interpretability of Visual Explanations. ECCV (12) Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
2022	J	`jnl`	Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation. CoRR Indu Panigrahi, Ryan Manzuk, Adam Maloof, Ruth Fong
2022	J	`jnl`	Interactive Visual Feature Search. CoRR Devon Ulrich, Ruth Fong
2022	J	`jnl`	Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability. CoRR Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
2022	—	`ed.`	xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers xxAI@ICML Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek
2021	J	`jnl`	HIVE: Evaluating the Human Interpretability of Visual Explanations. CoRR Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
2021	A*	`conf`	On Compositions of Transformations in Contrastive Self-Supervised Learning. ICCV Mandela Patrick, Yuki Markus Asano, Polina Kuznetsova, Ruth Fong, João F. Henriques, Geoffrey Zweig, Andrea Vedaldi
2020	—	`conf`	Contextual Semantic Interpretability. ACCV (4) Diego Marcos, Ruth Fong, Sylvain Lobry, Rémi Flamary, Nicolas Courty, Devis Tuia
2020	J	`jnl`	Contextual Semantic Interpretability. CoRR Diego Marcos, Ruth Fong, Sylvain Lobry, Rémi Flamary, Nicolas Courty, Devis Tuia
2020	J	`jnl`	Debiasing Convolutional Neural Networks via Meta Orthogonalization. CoRR Kurtis Evan David, Qiang Liu, Ruth Fong
2020	J	`jnl`	Multi-modal Self-Supervision from Generalized Data Transformations. CoRR Mandela Patrick, Yuki Markus Asano, Ruth Fong, João F. Henriques, Geoffrey Zweig, Andrea Vedaldi
2020	A*	`conf`	Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning. NeurIPS Iro Laina, Ruth Fong, Andrea Vedaldi
2020	A*	`conf`	There and Back Again: Revisiting Backpropagation Saliency Methods. CVPR Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi
2020	J	`jnl`	There and Back Again: Revisiting Backpropagation Saliency Methods. CoRR Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi
2020	J	`jnl`	Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. CoRR Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian K. Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif M. Khan, Yoshua Bengio, Markus Anderljung
2020	—	`—`	Understanding convolutional neural networks. Ruth Fong
2020	—	`conf`	xxAI - Beyond Explainable Artificial Intelligence. xxAI@ICML Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek
2019	—	`ch.`	Explanations for Attributing Deep Neural Network Predictions. Explainable AI Ruth Fong, Andrea Vedaldi
2019	J	`jnl`	NormGrad: Finding the Pixels that Matter for Training. CoRR Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Hakan Bilen, Andrea Vedaldi
2019	—	`conf`	Occlusions for Effective Data Augmentation in Image Classification. ICCV Workshops Ruth Fong
2019	J	`jnl`	Occlusions for Effective Data Augmentation in Image Classification. CoRR Ruth Fong, Andrea Vedaldi
2019	A*	`conf`	Understanding Deep Networks via Extremal Perturbations and Smooth Masks. ICCV Ruth Fong, Mandela Patrick, Andrea Vedaldi
2019	J	`jnl`	Understanding Deep Networks via Extremal Perturbations and Smooth Masks. CoRR Ruth Fong, Mandela Patrick, Andrea Vedaldi
2018	A*	`conf`	Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks. CVPR Ruth Fong, Andrea Vedaldi
2018	J	`jnl`	Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks. CoRR Ruth Fong, Andrea Vedaldi
2017	J	`jnl`	Interpretable Explanations of Black Boxes by Meaningful Perturbation. CoRR Ruth Fong, Andrea Vedaldi
2017	J	`jnl`	Using Human Brain Activity to Guide Machine Learning. CoRR Ruth Fong, Walter J. Scheirer, David D. Cox