9 citations · 17 across the 3 of their papers we have counts for
5 papers
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Shengyao Lu, Bang Liu, Keith G. Mills +2
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with st…
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…
Android Malware Detection based on Factorization Machine
Chenglin Li, Keith Mills, Rui Zhu +3
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users fac…