Song Han

DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow.

2016 · arXiv (Cornell University)

Modern deep neural networks have a large number of parameters, making them very powerful machine learning systems. A critical issue for training such large networks on large-scale data-sets is to prevent overfitting while at the …

Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning

2020 · arXiv (Cornell University)

We present Tiny-Transfer-Learning (TinyTL), an efficient on-device learning method to adapt pre-trained models to newly collected data on edge devices. Different from conventional transfer learning methods that fine-tune the full network or the last layer, …

NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms

2022 · arXiv (Cornell University)

Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We …