Bryan Lim

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 313 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #27,191 of 35,345 by trust here, #29,412 by citations
• Management Science and Operations Research — 49 citations into it; #4,933 of 11,946 by trust here, #2,957 by citations
• Time-Series & Forecasting — 46 citations into it; #145 of 381 by trust here, #103 by citations
• Signal Processing — 45 citations into it; #3,555 of 7,572 by trust here, #2,664 by citations
• Model Architectures — 43 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Economics and Econometrics — 26 citations into it
• Statistics and Probability — 26 citations into it

Whom Bryan Lim trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Alex Krizhevsky
7. J. Urgen Schmidhuber
8. Yann LeCun
9. Alex Graves
10. Oriol Vinyals

Who trusts Bryan Lim overall

1. Ioana Bica
2. Ahmed M. Alaa
3. Stefan Zohren
4. Si Chen
5. Liuyi Yao
6. Mihaela van der Schaar
7. Guillermo Alonso Iglesias
8. Qingsong Wen
9. Xiaoyong Jin
10. Chun-Liang Li

Papers from Bryan Lim's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting (2019) — 2,901 citations
• Time-series forecasting with deep learning: a survey (2020) — 2,001 citations
• Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018) — 39 citations
• Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019) — 51 citations
• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019) — 103 citations
• Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks (2018) — 198 citations