855 citations · 3.9k across the 142 of their papers we have counts for
10 papers · 1 filter
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
Lei Gao, Amir Ziashahabi, Yue Niu +2
Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned w…
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…
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Erum Mushtaq, Duygu Nur Yaldiz, Yavuz Faruk Bakman +4
Continual self-supervised learning (CSSL) learns a series of tasks sequentially on the unlabeled data. Two main challenges of continual learning are catastrophic forgetting and tas…
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates +5
Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences. A significant subset of UE methods utilize…
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
Mengwei Yang, Ismat Jarin, Baturalp Buyukates +2
Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. One key challenge in practical FL systems is data heterogeneity, particu…
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers
Tuo Zhang, Jinyue Yuan, Salman Avestimehr
Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. In particular, the Optimization by PROmpting (OPRO) approach provides…