3 citations · 5 across the 5 of their papers we have counts for
5 papers
Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield +1
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use…
Bias, Accuracy, and Trust: Gender-Diverse Perspectives on Large Language Models
Aimen Gaba, Emily Wall, Tejas Ramkumar Babu +3
Large language models (LLMs) are becoming increasingly ubiquitous in our daily lives, but numerous concerns about bias in LLMs exist. This study examines how gender-diverse populat…
Memory Savings at What Cost? A Study of Alternatives to Backpropagation
Kunjal Panchal, Sunav Choudhary, Yuriy Brun +1
Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language m…
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
Kunjal Panchal, Nisarg Parikh, Sunav Choudhary +3
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using pr…
Attack-Resilient Image Watermarking Using Stable Diffusion
Lijun Zhang, Xiao Liu, Antoni Viros Martin +3
Watermarking images is critical for tracking image provenance and proving ownership. With the advent of generative models, such as stable diffusion, that can create fake but realis…