Olatunji Ruwase

Artificial Intelligence · 2004–2024

A researcher in the Science corpus: 13 papers; trusts 49 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 243 citations into it; #15,382 of 35,345 by trust here, #3,279 by citations
• Computer Vision and Pattern Recognition — 97 citations into it; #15,142 of 21,575 by trust here, #4,001 by citations
• Large Language Models — 91 citations into it
• Efficiency & Compression — 58 citations into it; #215 of 1,264 by trust here, #83 by citations
• Optimization & Training — 54 citations into it; #2,472 of 2,601 by trust here, #550 by citations
• Hardware and Architecture — 28 citations into it; #294 of 1,551 by trust here, #62 by citations
• Information Systems — 15 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Olatunji Ruwase trusts overall

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

1. Diederik P. Kingma
2. Derek Bruening
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Noam Shazeer
6. George C. Necula
7. Vladimir Kiriansky
8. Alec Radford
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Olatunji Ruwase overall

1. James Newsome
2. Sandeep Bhatkar
3. Daniel C. DuVarney
4. Wei Xu
5. Pradeep Varma
6. Dawn Song
7. László Szekeres
8. Samyam Rajbhandari
9. Vijay Anand Korthikanti
10. Miguel Oom Temudo de Castro

Papers from Olatunji Ruwase's perspective

Ordered by how strongly the researchers Olatunji Ruwase 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. Learning Multiple Layers of Features from Tiny Images
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Going deeper with convolutions

Their papers

• ZeRO-Offload: Democratizing Billion-Scale Model Training (2021) — 618 citations
• 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- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale (2022) — 640 citations
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 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
• Accelerating Deep Convolutional Neural Networks Using Specialized Hardware (2015) — 343 citations
• Toward Accelerating Deep Learning at Scale Using Specialized Logic (2015)
• Toward accelerating deep learning at scale using specialized hardware in the datacenter (2015) — 48 citations
• A Practical Dynamic Buffer Overflow Detector (2004) — 398 citations