Jonathan Goh

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #14,544 of 35,345 by trust here, #11,750 by citations
• Generative Models — 12 citations into it
• Computer Networks and Communications — 11 citations into it; #2,953 of 9,886 by trust here, #2,539 by citations
• Statistics and Probability — 9 citations into it
• Control and Systems Engineering — 8 citations into it; #2,698 of 8,257 by trust here, #2,692 by citations
• Analytical Chemistry — 6 citations into it

Whom Jonathan Goh trusts overall

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

1. Richard P. Lippmann
2. Individualized Treat
3. Xuewu Dai
4. Lovekesh Vig
5. Thomas Schlegl
6. Joshua W. Haines

Who trusts Jonathan Goh overall

1. Dan Li
2. Moshe Kravchik
3. Ahmed Abdulaal
4. Dacheng Chen
5. Asaf Shabtai
6. Zhuanghua Liu
7. Astha Garg
8. Laura Erhan
9. Tomer Lancewicki
10. See-Kiong Ng

Papers from Jonathan Goh's perspective

Ordered by how strongly the researchers Jonathan Goh trusts endorse each paper. Same corpus, a different order for every researcher.

1. Improved Techniques for Training GANs
2. GENERATIVE ADVERSARIAL NETS
3. Long Short Term Memory Networks for Anomaly Detection in Time Series (The European Symposium on Artificial Neural Networks)
4. Generative Moment Matching Networks
5. Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition (IEEE International Conference on Acoustics, Speech, and Signal Processing)
6. Lessons Learned from the Maroochy Water Breach (Critical Infrastructure Protection)
7. Feature bagging for outlier detection (Knowledge Discovery and Data Mining)
8. The 1999 DARPA off-line intrusion detection evaluation (Comput. Networks)
9. LOF: identifying density-based local outliers
10. MMD GAN: Towards Deeper Understanding of Moment Matching Network

Their papers

• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks (2019) — 1,053 citations
• Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018) — 329 citations
• Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks (2017) — 379 citations
• A Dataset to Support Research in the Design of Secure Water Treatment Systems (2016) — 710 citations