Bharat Kaul

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 126 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #30,772 of 35,345 by trust here, #11,072 by citations
• Computer Vision and Pattern Recognition — 87 citations into it; #18,852 of 21,575 by trust here, #6,281 by citations
• Efficiency & Compression — 46 citations into it
• Reinforcement Learning — 27 citations into it
• Optimization & Training — 22 citations into it
• Model Architectures — 18 citations into it
• Natural Language Processing — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Bharat Kaul trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Andrew Y. Ng
9. Alex Graves
10. Ross B. Girshick

Who trusts Bharat Kaul overall

1. Asit K. Mishra
2. Debbie Marr

Papers from Bharat Kaul's perspective

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

Their papers

• Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers (2018) — 267 citations
• 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
• RAIL: Risk-Averse Imitation Learning (2017) — 21 citations
• SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training (2020) — 515 citations
• Mixed Precision Training With 8-bit Floating Point (2019) — 80 citations