Yingqian Zhang
Artificial Intelligence · with Verwer · 2009–2020
A researcher in the Science corpus: 8 papers; trusts 58 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 70 citations into it; #3,372 of 35,345 by trust here, #10,065 by citations
- Algorithms & Complexity — 26 citations into it
- Information Systems — 20 citations into it; #1,086 of 8,771 by trust here, #3,640 by citations
- Control and Systems Engineering — 18 citations into it
- Optimization & Training — 17 citations into it; #693 of 2,601 by trust here, #1,585 by citations
- Statistical Learning Theory — 15 citations into it; #208 of 1,038 by trust here, #413 by citations
- Representation & Self-Supervised Learning — 15 citations into it
- Computational Theory and Mathematics — 13 citations into it
Whom Yingqian Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sicco Verwer
- J. Ross Quinlan
- Niko Speybroeck
- Dimitris J. Bertsimas
- TreesKristin P. Bennett
- A. Pedro Duarte Silva
- Rafael Blanquero
- Geoffrey E. Hinton
- Christian Bessiere
- Diederik P. Kingma
Who trusts Yingqian Zhang overall
- Emir Demirovi'c
- Sicco Verwer
- Vinícius G. Costa
- Murat Firat
- Haoran Zhu
- Cynthia Rudin
- Sina Aghaei
- Rafael Blanquero
- Emilio Carrizosa
- Carlos Eduardo Pedreira
Papers from Yingqian Zhang's perspective
Ordered by how strongly the researchers Yingqian Zhang 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- GENERATIVE ADVERSARIAL NETS
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning fuzzy decision trees using integer programming (2018) — 11 citations
- Auction optimization using regression trees and linear models as integer programs (2014) — 63 citations
- Column generation based heuristic for learning classification trees (2020) — 28 citations
- Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019) — 189 citations
- Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019) — 346 citations
- Constructing classification trees using column generation (2018) — 7 citations
- Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization (2017) — 71 citations
- On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences (2009) — 96 citations