AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
At 460 h of clean LibriSpeech, the seed moves fairness metrics more than compression does on most demographic axes, and a balanced 3x3 decomposition attributes 85.3% of the variation in Fair-Speech ethnicity normalized gap to the seed against 8.3% to compression.
Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hypothesis is that models struggle to integrate multiple components, leading to training interventions to improve compositional binding. However, this assumption has never...
It is shown that single-snapshot fairness audits on full-precision models do not capture the deployment-time burden that compression places on already-marginalized speakers, and cast the temporal-taxation construct of Choi and Choi (2025) as a quantitative metric.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.