Yelysei Bondarenko

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 139 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #32,412 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 65 citations into it
• Efficiency & Compression — 48 citations into it; #1,146 of 1,264 by trust here, #913 by citations
• Model Architectures — 23 citations into it
• Natural Language Processing — 12 citations into it
• Optimization & Training — 9 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Yelysei Bondarenko trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Raghuraman Krishnamoorthi
5. Markus Nagel
6. Alex Krizhevsky
7. Kaiming He
8. Ilya Sutskever
9. Jimmy Ba
10. Yann LeCun

Who trusts Yelysei Bondarenko overall

1. Sehoon Kim
2. Guangxuan Xiao
3. Zi-Mei Yao
4. Coleman Hooper
5. Tim Dettmers
6. Ji Lin
7. Mingjie Sun
8. Reza Yazdani Aminabadi

Papers from Yelysei Bondarenko's perspective

Ordered by how strongly the researchers Yelysei Bondarenko 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021) — 196 citations
• Overcoming Oscillations in Quantization-Aware Training (2022) — 171 citations
• A White Paper on Neural Network Quantization (2021) — 894 citations