Mohammad Shoeybi

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 21 papers; trusts 402 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 694 citations into it; #7,749 of 35,345 by trust here, #1,260 by citations
• Natural Language Processing — 358 citations into it; #3,841 of 5,688 by trust here, #981 by citations
• Computer Vision and Pattern Recognition — 287 citations into it; #20,597 of 21,575 by trust here, #3,057 by citations
• Large Language Models — 234 citations into it
• Evaluation & Benchmarks — 146 citations into it
• Model Architectures — 83 citations into it; #2,135 of 2,278 by trust here, #2,037 by citations
• Efficiency & Compression — 81 citations into it; #422 of 1,264 by trust here, #67 by citations
• Safety, Robustness & Interpretability — 74 citations into it

Whom Mohammad Shoeybi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Alec Radford
7. Kaiming He
8. Yann LeCun
9. Jimmy Ba
10. Quoc V. Le

Who trusts Mohammad Shoeybi overall

1. Samyam Rajbhandari
2. Tri Dao
3. Pengcheng He
4. Devendra Singh Sachan
5. Minjia Zhang
6. M. Patwary
7. Dani Yogatama
8. D. Narayanan
9. Erik Nijkamp
10. Xiaodong Liu

Papers from Mohammad Shoeybi's perspective

Ordered by how strongly the researchers Mohammad Shoeybi 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. 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (2019) — 2,986 citations
• Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model (2022) — 859 citations
• Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM (2021) — 1,287 citations
• Deep Voice: Real-time Neural Text-to-Speech (2017) — 666 citations
• Factuality Enhanced Language Models for Open-Ended Text Generation (2022) — 310 citations
• Reducing Activation Recomputation in Large Transformer Models (2022) — 492 citations
• VILA: On Pre-training for Visual Language Models (2023) — 896 citations
• Long-Short Transformer: Efficient Transformers for Language and Vision (2021) — 175 citations
• Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases (2021) — 27 citations
• FP8 Formats for Deep Learning (2022) — 285 citations
• Controllable Story Generation with External Knowledge Using Large-Scale Language Models (2020) — 140 citations
• Bio-Megatron: Larger Biomedical Domain Language Model (2020) — 177 citations
• ODIN: Disentangled Reward Mitigates Hacking in RLHF (2024) — 129 citations
• NVLM: Open Frontier-Class Multimodal LLMs (2024)
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models (2024) — 542 citations
• ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities (2024)
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models (2022) — 90 citations
• End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021) — 110 citations
• Efficient Large-Scale Language Model Training on GPU Clusters (2021) — 108 citations