Raghavendra Chalapathy

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 123 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #8,042 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Computer Networks and Communications — 45 citations into it; #1,468 of 9,886 by trust here, #1,744 by citations
• Signal Processing — 29 citations into it; #2,312 of 7,572 by trust here, #2,343 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Natural Language Processing — 14 citations into it
• Model Architectures — 10 citations into it
• Information Systems — 9 citations into it

Whom Raghavendra Chalapathy trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Sepp Hochreiter
7. J. Urgen Schmidhuber
8. Yann LeCun
9. Christopher K. I. Williams
10. Ronan Collobert

Who trusts Raghavendra Chalapathy overall

1. Vikash Sehwag
2. Hongzhi Wang
3. Lukas Ruff
4. Wenpeng Hu
5. Pramuditha Perera
6. Haixuan Guo
7. Andrew Cook
8. Yu-xin Zhang
9. Bo Li
10. Qi Wang

Papers from Raghavendra Chalapathy's perspective

Ordered by how strongly the researchers Raghavendra Chalapathy 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Learning for Anomaly Detection: A Survey (2019) — 1,857 citations
• Bidirectional LSTM-CRF for Clinical Concept Extraction (2016) — 97 citations
• Anomaly Detection using One-Class Neural Networks (2018) — 422 citations