Amirkeivan Mohtashami

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 4 papers; trusts 86 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #19,871 of 35,345 by trust here, #19,873 by citations
• Large Language Models — 42 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it; #12,403 of 21,575 by trust here, #14,822 by citations
• Model Architectures — 32 citations into it; #1,629 of 2,278 by trust here, #1,882 by citations
• Efficiency & Compression — 22 citations into it
• Natural Language Processing — 16 citations into it
• Optimization & Training — 16 citations into it; #2,242 of 2,601 by trust here, #2,568 by citations
• Evaluation & Benchmarks — 15 citations into it

Whom Amirkeivan Mohtashami trusts overall

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

1. Alex Krizhevsky
2. L. Eon Bottou
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yoshua Bengio
6. Diederik P. Kingma
7. Sergey Ioffe
8. Christian Szegedy
9. Yann LeCun
10. Kaiming He

Who trusts Amirkeivan Mohtashami overall

1. Shouyuan Chen
2. Yucheng Lu
3. Liang Wang
4. Coleman Hooper
5. Cheng-Ping Hsieh
6. Yiran Ding
7. Albert Qiaochu Jiang
8. Sherman Wong
9. Wentao Guo
10. Sehoon Kim

Papers from Amirkeivan Mohtashami's perspective

Ordered by how strongly the researchers Amirkeivan Mohtashami 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Landmark Attention: Random-Access Infinite Context Length for Transformers (2023) — 222 citations
• QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs (2024) — 576 citations
• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models (2023) — 399 citations
• Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods (2022) — 15 citations