19 citations · 20 across the 6 of their papers we have counts for
6 papers
Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
Tianshuo Xu, Peng Mi, Ruilin Wang +1
Diffusion models (DMs) are a powerful generative framework that have attracted significant attention in recent years. However, the high computational cost of training DMs limits th…
Neural auto-designer for enhanced quantum kernels
Cong Lei, Yuxuan Du, Peng Mi +2
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum…
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer
Peng Mi, Li Shen, Tianhe Ren +6
Deep neural networks often suffer from poor generalization due to complex and non-convex loss landscapes. Sharpness-Aware Minimization (SAM) is a popular solution that smooths the…
State space models can express n-gram languages
Vinoth Nandakumar, Qiang Qu, Peng Mi +1
Recent advancements in recurrent neural networks (RNNs) have reinvigorated interest in their application to natural language processing tasks, particularly with the development of…
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models
Qihuang Zhong, Liang Ding, Li Shen +4
Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware mini…
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization
Tianshuo Xu, Lijiang Li, Peng Mi +5
PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem…