Neeraj Dhungel

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 162 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #14,445 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 24 citations into it; #9,338 of 21,575 by trust here, #10,154 by citations
• Model Architectures — 11 citations into it
• Pulmonary and Respiratory Medicine — 8 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Neeraj Dhungel trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Justin Domke
6. Ilya Sutskever
7. Ross B. Girshick
8. Martin J. Wainwright
9. Vinod Nair
10. Diederik P. Kingma

Who trusts Neeraj Dhungel overall

1. Wentao Zhu
2. Mugahed A. Al-antari
3. Gustavo Carneiro
4. Dina Ahmed Ragab
5. Xiaohui S. Xie
6. Mohammed A. Al-masni
7. Andrew P. Bradley
8. Moi Hoon Yap
9. Geert J. S. Litjens
10. Ghulam Murtaza

Papers from Neeraj Dhungel's perspective

Ordered by how strongly the researchers Neeraj Dhungel 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A deep learning approach for the analysis of masses in mammograms with minimal user intervention (2017) — 302 citations
• The Automated Learning of Deep Features for Breast Mass Classification from Mammograms (2016) — 140 citations
• Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms (2015) — 161 citations