9 citations · 21 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is…
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
Sara Babakniya, Ahmed Roushdy Elkordy, Yahya H. Ezzeldin +4
Transfer learning via fine-tuning pre-trained transformer models has gained significant success in delivering state-of-the-art results across various NLP tasks. In the absence of c…
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learnin…
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Sara Babakniya, Souvik Kundu, Saurav Prakash +2
Edge devices can benefit remarkably from federated learning due to their distributed nature; however, their limited resource and computing power poses limitations in deployment. A…