Reza Yazdani Aminabadi

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 5 papers; trusts 52 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #22,306 of 35,345 by trust here, #4,101 by citations
• Computer Vision and Pattern Recognition — 92 citations into it; #13,913 of 21,575 by trust here, #3,323 by citations
• Efficiency & Compression — 78 citations into it; #785 of 1,264 by trust here, #196 by citations
• Natural Language Processing — 75 citations into it
• Large Language Models — 74 citations into it
• Optimization & Training — 23 citations into it

Whom Reza Yazdani Aminabadi trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Samyam Rajbhandari
5. Alec Radford
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Noam Shazeer

Who trusts Reza Yazdani Aminabadi overall

1. Elias Frantar
2. Ying Sheng
3. Zichang Liu
4. Tim Dettmers
5. Lianmin Zheng
6. Dan Alistarh
7. Yichao Fu
8. Zhuohan Li
9. Ji Lin
10. Artyom Eliseev

Papers from Reza Yazdani Aminabadi's perspective

Ordered by how strongly the researchers Reza Yazdani Aminabadi 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model (2022) — 859 citations
• ZeRO-Offload: Democratizing Billion-Scale Model Training (2021) — 618 citations
• DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale (2022) — 591 citations
• ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers (2022) — 759 citations
• DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale (2022) — 640 citations