Minjia Zhang

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 300 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 337 citations into it; #14,880 of 35,345 by trust here, #5,924 by citations
• Computer Vision and Pattern Recognition — 172 citations into it; #10,559 of 21,575 by trust here, #5,375 by citations
• Natural Language Processing — 155 citations into it
• Efficiency & Compression — 141 citations into it; #677 of 1,264 by trust here, #329 by citations
• Large Language Models — 135 citations into it
• Evaluation & Benchmarks — 67 citations into it
• Model Architectures — 65 citations into it
• Optimization & Training — 43 citations into it

Whom Minjia Zhang trusts overall

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

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

Who trusts Minjia Zhang overall

1. Mengzhou Xia
2. Elias Frantar
3. Sehoon Kim
4. Tianyu Gao
5. Tim Dettmers
6. Ying Sheng
7. Dan Alistarh
8. Zhiyuan Zeng

Papers from Minjia Zhang's perspective

Ordered by how strongly the researchers Minjia Zhang 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• 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
• Learning Intrinsic Sparse Structures within Long Short-term Memory (2017) — 144 citations
• Extreme Compression for Pre-trained Transformers Made Simple and Efficient (2022) — 31 citations
• Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping (2020) — 124 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning (2021) — 92 citations