Daniel L. Marino

Electrical and Electronic Engineering · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 10 citations into it; #4,394 of 16,539 by trust here, #8,802 by citations
• Artificial Intelligence — 9 citations into it
• Model Architectures — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it; #9,865 of 21,575 by trust here, #12,419 by citations
• Optimization & Training — 5 citations into it

Whom Daniel L. Marino trusts overall

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

1. Diederik P. Kingma
2. Paul J. Werbos
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ronald J. Williams
6. Yann LeCun
7. Jimmy Ba
8. Elena Mocanu
9. Graham W. Taylor
10. Razvan Pascanu

Who trusts Daniel L. Marino overall

1. Alberto Gasparin
2. Kasun Amarasinghe
3. T. Kim
4. Milos Manic
5. Yi Wang
6. Salah Bouktif
7. H. Hua
8. Benjamin Lindemann
9. Slobodan Lukovic
10. Elena Mocanu

Papers from Daniel L. Marino's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Building energy load forecasting using Deep Neural Networks (2016) — 570 citations
• Deep neural networks for energy load forecasting (2017) — 305 citations