Naveen Mellempudi

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 49 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #30,765 of 35,345 by trust here, #13,962 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #18,853 of 21,575 by trust here, #7,584 by citations
• Efficiency & Compression — 48 citations into it
• Optimization & Training — 21 citations into it
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Natural Language Processing — 10 citations into it

Whom Naveen Mellempudi trusts overall

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

1. Kaiming He
2. Yoshua Bengio
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Matthieu Courbariaux
6. Itay Hubara
7. Jia Deng
8. X. Zhang
9. Ilya Sutskever
10. Alex Graves

Who trusts Naveen Mellempudi overall

1. Asit K. Mishra
2. Qing Jin
3. Christos Louizos
4. Léopold Cambier
5. N. Jouppi
6. Debbie Marr
7. Xiaoxia Wu
8. Liyuan Liu
9. Jungwook Choi
10. Yury Nahshan

Papers from Naveen Mellempudi's perspective

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

Their papers

• Ternary Neural Networks with Fine-Grained Quantization (2017) — 112 citations
• A Study of BFLOAT16 for Deep Learning Training (2019) — 492 citations
• Mixed Precision Training of Convolutional Neural Networks using Integer Operations (2018) — 169 citations
• FP8 Formats for Deep Learning (2022) — 285 citations
• Mixed Precision Training With 8-bit Floating Point (2019) — 80 citations