10 citations · 31 across the 14 of their papers we have counts for
4 papers · 1 filter
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
Tianshi Wang, Jinyang Li, Ruijie Wang +7
This paper introduces SudokuSens, a generative framework for automated generation of training data in machine-learning-based Internet-of-Things (IoT) applications, such that the ge…
State Action Separable Reinforcement Learning
Ziyao Zhang, Liang Ma, Kin K. Leung +2
Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulatio…
SENSE: Semantically Enhanced Node Sequence Embedding
Swati Rallapalli, Liang Ma, Mudhakar Srivatsa +4
Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single grap…
neuralRank: Searching and ranking ANN-based model repositories
Nirmit Desai, Linsong Chu, Raghu K. Ganti +2
Widespread applications of deep learning have led to a plethora of pre-trained neural network models for common tasks. Such models are often adapted from other models via transfer…