Akbar Siami Namin

Management Science and Operations Research · 2006–2019

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 19 citations into it; #6,506 of 11,946 by trust here, #7,638 by citations
• Artificial Intelligence — 18 citations into it
• Model Architectures — 12 citations into it
• Finance — 10 citations into it
• Economics and Econometrics — 8 citations into it
• Signal Processing — 6 citations into it; #5,210 of 7,572 by trust here, #6,836 by citations

Whom Akbar Siami Namin trusts overall

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

1. J. Urgen Schmidhuber
2. Alex Graves
3. Mehdi Khashei
4. M. Pesaran
5. Nicolas Huck
6. Sepp Hochreiter
7. Thabani Nyoni
8. Felix Alexander Gers
9. Mehdi Bijari
10. Geoffrey E. Hinton

Who trusts Akbar Siami Namin overall

1. Omer Berat Sezer
2. Mike Papadakis

Papers from Akbar Siami Namin's perspective

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

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
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. ImageNet: A large-scale hierarchical image database

Their papers

• Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018) — 279 citations
• A Comparison of ARIMA and LSTM in Forecasting Time Series (2018) — 1,065 citations
• The Performance of LSTM and BiLSTM in Forecasting Time Series (2019) — 1,361 citations
• Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria (2006) — 509 citations