17 citations · 36 across the 7 of their papers we have counts for
7 papers
NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks
Kang Yang, Kunhao Lai
Deep neural network (DNN) models have become a critical asset of the model owner as training them requires a large amount of resource (i.e. labeled data). Therefore, many fingerpri…
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
Guhong Nie, Lirui Xiao, Menglong Zhu +6
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and…
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
Xiang Huang, Hongsheng Liu, Beiji Shi +11
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a pro…
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Xiang Huang, Zhanhong Ye, Hongsheng Liu +11
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, bo…
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
Qigong Sun, Xiufang Li, Fanhua Shang +4
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage. Thus, DNNs cannot be efficiently applied to mobile phones and…
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
Qigong Sun, Fanhua Shang, Kang Yang +3
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones…
Intra-Ensemble in Neural Networks
Yuan Gao, Zixiang Cai, Lei Yu
Improving model performance is always the key problem in machine learning including deep learning. However, stand-alone neural networks always suffer from marginal effect when stac…