See-Kiong Ng

Artificial Intelligence · International Conference on Artificial Neural Networks · 2009–2023

A researcher in the Science corpus: 6 papers; trusts 133 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #20,854 of 35,345 by trust here, #12,580 by citations
• Natural Language Processing — 69 citations into it
• Molecular Biology — 61 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Evaluation & Benchmarks — 46 citations into it
• Computer Networks and Communications — 16 citations into it
• Signal Processing — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom See-Kiong Ng trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ilya Sutskever
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Alex Krizhevsky
7. Kyunghyun Cho
8. Richard Socher
9. Dzmitry Bahdanau
10. Alex Graves

Who trusts See-Kiong Ng overall

1. Potsawee Manakul
2. Yuheng Zha

Papers from See-Kiong Ng's perspective

Ordered by how strongly the researchers See-Kiong Ng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (2019) — 1,053 citations
• Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010) — 345 citations
• A core-attachment based method to detect protein complexes in PPI networks (2009) — 351 citations
• GPTScore: Evaluate as You Desire (2023) — 485 citations
• Polyglot Prompt: Multilingual Multitask PrompTraining (2022) — 16 citations
• Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018) — 329 citations