4 papers
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
Kareem Amin, Sara Babakniya, Alex Bie +3
Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown…
Clustering and Median Aggregation Improve Differentially Private Inference
Kareem Amin, Salman Avestimehr, Sara Babakniya +4
Differentially private (DP) language model inference is an approach for generating private synthetic text. A sensitive input example is used to prompt an off-the-shelf large langua…
Supervised Learning for Analog and RF Circuit Design: Benchmarks and Comparative Insights
Asal Mehradfar, Xuzhe Zhao, Yue Niu +4
Automating analog and radio-frequency (RF) circuit design using machine learning (ML) significantly reduces the time and effort required for parameter optimization. This study expl…
AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design
Asal Mehradfar, Xuzhe Zhao, Yue Niu +4
Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria like power consumption and bandwid…