Gopinath Chennupati

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Safety, Robustness & Interpretability — 19 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Natural Language Processing — 9 citations into it
• Model Architectures — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Gopinath Chennupati trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Karen Simonyan
6. Christian Szegedy
7. Ross B. Girshick
8. Yann LeCun
9. Diederik P. Kingma
10. Jia Deng

Who trusts Gopinath Chennupati overall

1. Jingkang Yang
2. Sudhanshu Mittal
3. Lilly Kumari

Papers from Gopinath Chennupati's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Combating Label Noise in Deep Learning Using Abstention (2019) — 196 citations
• On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks (2019) — 628 citations