Nikhil Naik

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 7 papers; trusts 694 researchers and is trusted by 130 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #15,199 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 100 citations into it; #2,474 of 35,345 by trust here, #3,703 by citations
• Model Architectures — 36 citations into it
• Optimization & Training — 35 citations into it
• Neural Architecture Search & AutoML — 19 citations into it; #16 of 283 by trust here, #6 by citations
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Computational Theory and Mathematics — 15 citations into it

Whom Nikhil Naik trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Bernhard Schölkopf
7. Karen Simonyan
8. Yann LeCun
9. Ilya Sutskever
10. Antonio Torralba

Who trusts Nikhil Naik overall

1. Martin Wistuba
2. Xuanyi Dong
3. Thomas Elsken
4. Barret Zoph
5. Yanan Sun
6. Zhaobai Zhong
7. Frank Hutter
8. Quoc V. Le
9. Quanming Yao
10. Hanxiao Liu

Papers from Nikhil Naik's perspective

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

Their papers

• Designing Neural Network Architectures using Reinforcement Learning (2016) — 1,553 citations
• Streetscore -- Predicting the Perceived Safety of One Million Streetscapes (2014) — 450 citations
• Practical Neural Network Performance Prediction for Early Stopping (2017) — 31 citations
• Improving Fine-Grained Visual Classification using Pairwise Confusion (2017) — 93 citations
• Deep Learning the City: Quantifying Urban Perception at a Global Scale (2016) — 540 citations
• Accelerating Neural Architecture Search using Performance Prediction (2017) — 390 citations
• Training with Confusion for Fine-Grained Visual Classification (2017) — 10 citations