Ross, Candace

Perturbation augmentation for fairer NLP

Qian, R., Ross, C., Fernandes, J., Smith, E. M., Kiela, D., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AITopic Modeling

Cited by 69

An Introduction to Vision-Language Modeling

Bordes, F., Pang, R. Y., Ajay, A., Li, A. C., Bardes, A., Petryk, S., Mañas, O., Lin, Z., Mahmoud, A., Jayaraman, B., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 47

CM3: A Causal Masked Multimodal Model of the Internet

Aghajanyan, A., Huang, B., Ross, C., Karpukhin, V., Xu, H., Goyal, N., Okhonko, D., Joshi, M., Ghosh, G., Lewis, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 42

Brittlebench: Quantifying LLM robustness via prompt sensitivity

Romanou, A., Ibrahim, M., Ross, C., Oktar, K., Shaib, C., Bell, S. J., Ovalle, A., Bosselut, A., Dodge, J., Sinha, K., et al. (2026) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 24

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Hall, M., Bell, S. J., Ross, C., Williams, A., Drozdzal, M., & Soriano, A. R. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency

Generative Adversarial Networks and Image SynthesisEthics and Social Impacts of AI

Cited by 11

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs

Krojer, B., Komeili, M., Ross, C., Garrido, Q., Sinha, K., Ballas, N., & Assran, M. (2025) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 0