28 citations · 85 across the 16 of their papers we have counts for
6 papers · 1 filter
Farthest Greedy Path Sampling for Two-shot Recommender Search
Yufan Cao, Tunhou Zhang, Wei Wen +3
Weight-sharing Neural Architecture Search (WS-NAS) provides an efficient mechanism for developing end-to-end deep recommender models. However, in complex search spaces, distinguish…
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Huanrui Yang, Minxue Tang, Wei Wen +5
Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a s…
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li +4
Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limit…
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang +5
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost. Traditionally, researchers manually craft deep neu…
AutoGrow: Automatic Layer Growing in Deep Convolutional Networks
Wei Wen, Feng Yan, Yiran Chen +1
Depth is a key component of Deep Neural Networks (DNNs), however, designing depth is heuristic and requires many human efforts. We propose AutoGrow to automate depth discovery in D…
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
Wei Wen, Yandan Wang, Feng Yan +4
In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a training method because of its efficiency; however, recently, a problem in SGD gains research interest:…