105 citations · 292 across the 10 of their papers we have counts for
22 papers
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Keith G. Mills, Fred X. Han, Jialin Zhang +6
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on desi…
LNAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Keith G. Mills, Fred X. Han, Mohammad Salameh +6
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures i…
Generative Adversarial Neural Architecture Search
Seyed Saeed Changiz Rezaei, Fred X. Han, Di Niu +5
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In t…
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
Mingjun Zhao, Shengli Yan, Bang Liu +6
Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that…
Neural Architecture Search For Keyword Spotting
Tong Mo, Yakun Yu, Mohammad Salameh +2
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural archit…
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models
Mingjun Zhao, Haijiang Wu, Di Niu +1
The competitive performance of neural machine translation (NMT) critically relies on large amounts of training data. However, acquiring high-quality translation pairs requires expe…