Xiaoping Liu
Economics and Econometrics · Environmental Research Letters · 2017–2021
A researcher in the Science corpus: 11 papers; trusts 19 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Economics and Econometrics — 100 citations into it; #20,720 of 23,481 by trust here, #7,262 by citations
- Global and Planetary Change — 70 citations into it
- Environmental Engineering — 67 citations into it
- Media Technology — 38 citations into it
- Health, Toxicology and Mutagenesis — 35 citations into it; #1,114 of 1,398 by trust here, #380 by citations
- Computer Vision and Pattern Recognition — 33 citations into it
- Artificial Intelligence — 30 citations into it
- Atmospheric Science — 30 citations into it
Whom Xiaoping Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter J. Rousseeuw
- Chih-Jen Lin
- Xia Li
- Fulong Wu
- Peter H. Verburg
- H. Geist
- Shaojian Wang
- David M. Blei
- Yanfei Zhong
- Alex Krizhevsky
Who trusts Xiaoping Liu overall
Papers from Xiaoping Liu's perspective
Ordered by how strongly the researchers Xiaoping Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities (2017) — 396 citations
- A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021) — 631 citations
- Global projections of future urban land expansion under shared socioeconomic pathways (2020) — 750 citations
- Mapping global urban boundaries from the global artificial impervious area (GAIA) data (2020) — 671 citations
- Projecting global urban land expansion and heat island intensification through 2050 (2019) — 358 citations
- Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach (2019) — 241 citations
- Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method (2018) — 441 citations
- Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model (2017) — 458 citations
- Classifying urban land use by integrating remote sensing and social media data (2017) — 328 citations
- A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects (2017) — 1,511 citations
- China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces (2017) — 262 citations