Jeff Rasley

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 9 papers; trusts 57 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 224 citations into it; #20,859 of 35,345 by trust here, #4,231 by citations
• Large Language Models — 94 citations into it
• Computer Vision and Pattern Recognition — 88 citations into it
• Efficiency & Compression — 64 citations into it; #272 of 1,264 by trust here, #105 by citations
• Optimization & Training — 46 citations into it; #2,584 of 2,601 by trust here, #712 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Management Science and Operations Research — 6 citations into it
• Neural Architecture Search & AutoML — 5 citations into it

Whom Jeff Rasley trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Frank Hutter
5. Yoshua Bengio
6. Ilya Sutskever
7. James Bergstra
8. Jasper Snoek
9. Yann LeCun
10. Jimmy Ba

Who trusts Jeff Rasley overall

1. Samyam Rajbhandari
2. Xiao Liu
3. Vijay Anand Korthikanti
4. Yuxin Fang
5. Quan-Sen Sun
6. Xiang Yue
7. Can Bülent Karakus
8. Xu Han
9. Zi-Mei Yao
10. Yizhong Wang

Papers from Jeff Rasley's perspective

Ordered by how strongly the researchers Jeff Rasley 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters (2020) — 2,160 citations
• ZeRO: Memory optimizations Toward Training Trillion Parameter Models (2019) — 1,913 citations
• ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep learning (2021) — 548 citations
• HyperDrive: exploring hyperparameters with POP scheduling (2017) — 41 citations
• DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale (2022) — 591 citations
• DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale (2022) — 640 citations
• Planck (2014) — 2,118 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• ZeRO: Memory Optimization Towards Training A Trillion Parameter Models (2019) — 616 citations