D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 31 Citations 4,565 101 World Ranking 7874 National Ranking 3659

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of study are Artificial intelligence, Pattern recognition, Contextual image classification, Machine learning and Computer vision. His Artificial intelligence research is multidisciplinary, incorporating elements of Algorithm and Natural language processing. His Pattern recognition research incorporates themes from Probability distribution, Image and Visual Word.

The Dimensionality reduction, MNIST database and Deep learning research Sicheng Zhao does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Block, therefore creating a link between diverse domains of science. His research in Computer vision intersects with topics in Emotion recognition and Data matching. He interconnects Point cloud and Rendering in the investigation of issues within Segmentation.

His most cited work include:

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
  • Auto-encoder based dimensionality reduction (194 citations)
  • Exploring Principles-of-Art Features For Image Emotion Recognition (168 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Pattern recognition, Machine learning, Image and Discriminative model are his primary areas of study. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Natural language processing. His Pattern recognition research includes themes of Contextual image classification, Salient, Margin and Visual Word.

His Machine learning research integrates issues from Feature extraction, Image retrieval and Set. As a member of one scientific family, he mostly works in the field of Image, focusing on Probability distribution and, on occasion, Categorical variable. Sicheng Zhao has researched Discriminative model in several fields, including Embedding and Feature.

He most often published in these fields:

  • Artificial intelligence (78.46%)
  • Pattern recognition (33.85%)
  • Machine learning (30.77%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (78.46%)
  • Domain adaptation (13.08%)
  • Machine learning (30.77%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Domain adaptation, Machine learning, Discriminative model and Pattern recognition. His Artificial intelligence study frequently draws parallels with other fields, such as Natural language processing. In general Machine learning study, his work on Convolutional neural network often relates to the realm of Variance, thereby connecting several areas of interest.

His studies examine the connections between Discriminative model and genetics, as well as such issues in Feature, with regards to Probability distribution and Visualization. The study incorporates disciplines such as Motion and Aggregate in addition to Pattern recognition. Sicheng Zhao works mostly in the field of Image, limiting it down to topics relating to Feature extraction and, in certain cases, Computational problem, Data science and Key, as a part of the same area of interest.

Between 2019 and 2021, his most popular works were:

  • Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning (29 citations)
  • Multi-source Distilling Domain Adaptation (20 citations)
  • Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey. (11 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His scientific interests lie mostly in Artificial intelligence, Domain adaptation, Labeled data, Machine learning and Multi-source. His research related to Image and Deep learning might be considered part of Artificial intelligence. His work deals with themes such as Training set and Benchmark, which intersect with Labeled data.

Sicheng Zhao works in the field of Machine learning, namely Discriminative model. His study in Pattern recognition extends to Multi-source with its themes. Many of his research projects under Pattern recognition are closely connected to Space and Volume with Space and Volume, tying the diverse disciplines of science together.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer.
arXiv: Computer Vision and Pattern Recognition (2018)

388 Citations

Auto-encoder based dimensionality reduction

Yasi Wang;Hongxun Yao;Sicheng Zhao.
Neurocomputing (2016)

278 Citations

SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

Bichen Wu;Xuanyu Zhou;Sicheng Zhao;Xiangyu Yue.
international conference on robotics and automation (2019)

220 Citations

Exploring Principles-of-Art Features For Image Emotion Recognition

Sicheng Zhao;Yue Gao;Xiaolei Jiang;Hongxun Yao.
acm multimedia (2014)

184 Citations

Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

Sicheng Zhao;Hongxun Yao;Yue Gao;Rongrong Ji.
IEEE Transactions on Multimedia (2017)

163 Citations

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Bichen Wu;Alvin Wan;Xiangyu Yue;Peter Jin.
computer vision and pattern recognition (2018)

138 Citations

SqueezeNext: Hardware-Aware Neural Network Design

Amir Gholami;Kiseok Kwon;Bichen Wu;Zizheng Tai.
computer vision and pattern recognition (2018)

126 Citations

Predicting Personalized Image Emotion Perceptions in Social Networks

Sicheng Zhao;Hongxun Yao;Yue Gao;Guiguang Ding.
IEEE Transactions on Affective Computing (2018)

119 Citations

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data

Xiangyu Yue;Yang Zhang;Sicheng Zhao;Alberto Sangiovanni-Vincentelli.
international conference on computer vision (2019)

99 Citations

Affective Image Retrieval via Multi-Graph Learning

Sicheng Zhao;Hongxun Yao;You Yang;Yanhao Zhang.
acm multimedia (2014)

93 Citations

Best Scientists Citing Sicheng Zhao

Kurt Keutzer

Kurt Keutzer

University of California, Berkeley

Publications: 36

Sebastien Ourselin

Sebastien Ourselin

King's College London

Publications: 17

Bjoern H. Menze

Bjoern H. Menze

University of Zurich

Publications: 15

Tom Vercauteren

Tom Vercauteren

King's College London

Publications: 15

Christos Davatzikos

Christos Davatzikos

University of Pennsylvania

Publications: 13

Jungong Han

Jungong Han

Aberystwyth University

Publications: 13

Hongxun Yao

Hongxun Yao

Harbin Institute of Technology

Publications: 12

Niraj K. Jha

Niraj K. Jha

Princeton University

Publications: 12

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 11

Klaus H. Maier-Hein

Klaus H. Maier-Hein

German Cancer Research Center

Publications: 11

Michael W. Mahoney

Michael W. Mahoney

University of California, Berkeley

Publications: 11

Shuihua Wang

Shuihua Wang

University of Leicester

Publications: 11

Jiebo Luo

Jiebo Luo

University of Rochester

Publications: 10

Mauricio Reyes

Mauricio Reyes

University of Bern

Publications: 10

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 10

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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