Yew-Soon Ong
Artificial Intelligence · 2003–2022
A researcher in the Science corpus: 25 papers; trusts 415 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 329 citations into it; #3,625 of 35,345 by trust here, #4,063 by citations
- Computational Theory and Mathematics — 177 citations into it; #569 of 12,182 by trust here, #669 by citations
- Computer Vision and Pattern Recognition — 90 citations into it
- Management Science and Operations Research — 70 citations into it
- Statistics and Probability — 66 citations into it
- Probabilistic & Bayesian Methods — 50 citations into it
- Natural Language Processing — 49 citations into it
- Statistical and Nonlinear Physics — 45 citations into it
Whom Yew-Soon Ong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kalyanmoy Deb
- Diederik P. Kingma
- Yoshua Bengio
- M. Powell
- Radford M. Neal
- Alex Krizhevsky
- Michael I. Jordan
- Yann LeCun
- Ilya Sutskever
Who trusts Yew-Soon Ong overall
- Ferrante Neri
- Yaochu Jin
- Abhishek Gupta
- Swagatam Das
- Bo Liu
- Handing Wang
- Carlos Cotta
- Xianshun Chen
- Liang Feng
- Rammohan Mallipeddi
Papers from Yew-Soon Ong's perspective
Ordered by how strongly the researchers Yew-Soon Ong 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- CoCon: A Self-Supervised Approach for Controlled Text Generation (2020) — 97 citations
- Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling (2003) — 607 citations
- Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder (2020) — 61 citations
- A Multi-Facet Survey on Memetic Computation (2011) — 502 citations
- Memetic Computation—Past, Present & Future [Research Frontier] (2010) — 391 citations
- Classification of adaptive memetic algorithms: a comparative study (2006) — 546 citations
- Meta-Lamarckian learning in memetic algorithms (2004) — 678 citations
- Consistencies and Contradictions of Performance Metrics in Multiobjective Optimization (2014) — 306 citations
- Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017) — 378 citations
- Multifactorial Evolution: Toward Evolutionary Multitasking (2016) — 845 citations
- Insights on Transfer Optimization: Because Experience is the Best Teacher (2018) — 325 citations
- Generalizing Surrogate-Assisted Evolutionary Computation (2010) — 445 citations
- When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018) — 875 citations
- A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017) — 421 citations
- Remarks on multi-output Gaussian process regression (2018) — 309 citations
- Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (2007) — 469 citations
- Markov blanket-embedded genetic algorithm for gene selection (2007) — 472 citations
- Extreme Learning Machines [Trends & Controversies] (2013) — 433 citations
- A fast pruned-extreme learning machine for classification problem (2008) — 367 citations
- Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction (2022) — 319 citations