47 citations · 50 across the 5 of their papers we have counts for
6 papers
Looking Into The Past: The Impact of Context on Detecting Deceptive Statements
Alex Clay, Ernesto Jimenez-Ruiz, Pranava Madhyastha
Deception detection typically considers messages in isolation. Previous works in claim verification and role detection have displayed that additional information such as gameplay h…
Bias at the End of the Score
Salma Abdel Magid, Grace Guo, Esin Tureci +4
Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have becom…
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models
Salma Abdel Magid, Weiwei Pan, Simon Warchol +4
Text-to-image (T2I) models are increasingly used in impactful real-life applications. As such, there is a growing need to audit these models to ensure that they generate desirable,…
They're All Doctors: Synthesizing Diverse Counterfactuals to Mitigate Associative Bias
Salma Abdel Magid, Jui-Hsien Wang, Kushal Kafle +1
Vision Language Models (VLMs) such as CLIP are powerful models; however they can exhibit unwanted biases, making them less safe when deployed directly in applications such as text-…
Masked Image Training for Generalizable Deep Image Denoising
Haoyu Chen, Jinjin Gu, Yihao Liu +5
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method f…
Revisiting RCAN: Improved Training for Image Super-Resolution
Zudi Lin, Prateek Garg, Atmadeep Banerjee +6
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this…