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From the 1 of 1.9k papers with an AI index.

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20032026
most citedReview of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1k citations

Showing 2019 · cs.LGShow all

11 papers · 2 filters

cs.LG201920 cited

Towards Unified INT8 Training for Convolutional Neural Network

Feng Zhu, Ruihao Gong, Fengwei Yu +5

Recently low-bit (e.g., 8-bit) network quantization has been extensively studied to accelerate the inference. Besides inference, low-bit training with quantized gradients can furth…

cs.LG20197 cited

Graph Markov Network for Traffic Forecasting with Missing Data

Zhiyong Cui, Longfei Lin, Ziyuan Pu +1

Traffic forecasting is a classical task for traffic management and it plays an important role in intelligent transportation systems. However, since traffic data are mostly collecte…

cs.LG2019

Structure of Deep Neural Networks with a Priori Information in Wireless Tasks

Jia Guo, Chenyang Yang

Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction. E…

cs.LG2019

Regularized Non-negative Spectral Embedding for Clustering

Yifei Wang, Rui Liu, Yong Chen +2

Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets. The algorithms in the Spectral Clustering family typically consist of…

cs.LG2019

Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling

Hekai Zhang, Jibing Gong, Zhiyong Teng +4

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute…

cs.LG201916 cited

MixUp as Directional Adversarial Training

Guillaume P. Archambault, Yongyi Mao, Hongyu Guo +1

In this work, we explain the working mechanism of MixUp in terms of adversarial training. We introduce a new class of adversarial training schemes, which we refer to as directional…