Kasun Bandara

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 104 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it
• Management Science and Operations Research — 76 citations into it; #4,899 of 11,946 by trust here, #5,316 by citations
• Signal Processing — 50 citations into it; #4,851 of 7,572 by trust here, #5,829 by citations
• Model Architectures — 45 citations into it
• Time-Series & Forecasting — 36 citations into it
• Economics and Econometrics — 29 citations into it; #14,724 of 23,481 by trust here, #18,597 by citations
• Statistics and Probability — 25 citations into it
• Optimization & Training — 20 citations into it

Whom Kasun Bandara trusts overall

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

1. Rob J Hyndman
2. Robert E. Schapire
3. Weizhong Yan
4. T. Warren Liao
5. Ronald J. Williams
6. Sven F. Crone
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. A. Robinson
10. Jasper Snoek

Who trusts Kasun Bandara overall

1. Pedro Lara-Benítez
2. Hansika Hewamalage
3. Mayur Wankhade
4. Manuel Carranza-García
5. Christoph Bergmeir
6. A. C. Rao
7. Konstantinos Benidis
8. Muhammad Mohsin Waqas

Papers from Kasun Bandara's perspective

Ordered by how strongly the researchers Kasun Bandara 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017) — 428 citations
• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019) — 1,252 citations
• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017) — 32 citations