50 citations · 389 across the 39 of their papers we have counts for
6 papers · 1 filter
TUTOR: Training Neural Networks Using Decision Rules as Model Priors
Shayan Hassantabar, Prerit Terway, Niraj K. Jha
The human brain has the ability to carry out new tasks with limited experience. It utilizes prior learning experiences to adapt the solution strategy to new domains. On the other h…
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
Shayan Hassantabar, Xiaoliang Dai, Niraj K. Jha
Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks…
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and signif…
SCANN: Synthesis of Compact and Accurate Neural Networks
Shayan Hassantabar, Zeyu Wang, Niraj K. Jha
Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design…
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
Hongxu Yin, Guoyang Chen, Yingmin Li +3
Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promisin…
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm
Xiaoliang Dai, Hongxu Yin, Niraj K. Jha
Deep neural networks (DNNs) have begun to have a pervasive impact on various applications of machine learning. However, the problem of finding an optimal DNN architecture for large…