193 citations · 351 across the 14 of their papers we have counts for
14 papers
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement
Andong Li, Shan You, Guochen Yu +2
While the deep learning techniques promote the rapid development of the speech enhancement (SE) community, most schemes only pursue the performance in a black-box manner and lack a…
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng +4
In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules ou…
Prioritized Architecture Sampling with Monto-Carlo Tree Search
Xiu Su, Tao Huang, Yanxi Li +5
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. Ho…
Locally Free Weight Sharing for Network Width Search
Xiu Su, Shan You, Tao Huang +4
Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluat…
Data Agnostic Filter Gating for Efficient Deep Networks
Xiu Su, Shan You, Tao Huang +5
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Curren…
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Yibo Yang, Hongyang Li, Shan You +3
Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods…
Quantum circuit architecture search for variational quantum algorithms
Yuxuan Du, Tao Huang, Shan You +2
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results e…
On the learnability of quantum neural networks
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +2
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme, which remains largely unknown due to the non-convex optim…
Quantum Differentially Private Sparse Regression Learning
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu +2
The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differe…
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
Shan You, Tao Huang, Mingmin Yang +3
Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current meth…
Bringing Giant Neural Networks Down to Earth with Unlabeled Data
Yehui Tang, Shan You, Chang Xu +2
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones. During the compressing process, one of the most imp…
Privileged Multi-label Learning
Shan You, Chang Xu, Yunhe Wang +2
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individ…
Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Yunhe Wang, Chang Xu, Shan You +2
Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recove…
Streaming Label Learning for Modeling Labels on the Fly
Shan You, Chang Xu, Yunhe Wang +2
It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning proce…