28 citations · 72 across the 6 of their papers we have counts for
6 papers
Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?
Albert Yu Sun, Eliott Zemour, Arushi Saxena +4
Machine learning practitioners often fine-tune generative pre-trained models like GPT-3 to improve model performance at specific tasks. Previous works, however, suggest that fine-t…
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
Jianhui Li, Zhennan Qin, Yijie Mei +11
With the rapid development of deep learning models and hardware support for dense computing, the deep learning workload characteristics changed significantly from a few hot spots o…
Global Optimization of Data Pipelines in Heterogeneous Cloud Environments
Erica Lin, Luna Xu, Suraj Bramhavar +5
Modern production data processing and machine learning pipelines on the cloud are critical components for many cloud-based companies. These pipelines are typically composed of comp…
TinyFedTL: Federated Transfer Learning on Tiny Devices
Kavya Kopparapu, Eric Lin
TinyML has rose to popularity in an era where data is everywhere. However, the data that is in most demand is subject to strict privacy and security guarantees. In addition, the de…
FedCD: Improving Performance in non-IID Federated Learning
Kavya Kopparapu, Eric Lin, Jessica Zhao
Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model. However, learning from real-world data ca…
FedFMC: Sequential Efficient Federated Learning on Non-iid Data
Kavya Kopparapu, Eric Lin
As a mechanism for devices to update a global model without sharing data, federated learning bridges the tension between the need for data and respect for privacy. However, classic…