Saleh Ashkboos

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 5 papers; trusts 37 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #27,590 of 35,345 by trust here, #10,798 by citations
• Efficiency & Compression — 55 citations into it; #982 of 1,264 by trust here, #633 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #17,103 of 21,575 by trust here, #9,187 by citations
• Large Language Models — 39 citations into it; #571 of 799 by trust here, #544 by citations
• Model Architectures — 19 citations into it
• Natural Language Processing — 19 citations into it
• Electrical and Electronic Engineering — 12 citations into it
• Optimization & Training — 11 citations into it

Whom Saleh Ashkboos trusts overall

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

1. Elias Frantar
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Alex Krizhevsky
6. Yoshua Bengio
7. Jimmy Ba
8. Diederik P. Kingma
9. Alec Radford
10. Dan Alistarh

Who trusts Saleh Ashkboos overall

1. Tim Dettmers
2. Zechun Liu
3. Ji Lin
4. Jerry Chee
5. Xunyu Zhu
6. Changsheng Zhao
7. Xinyin Ma
8. Artyom Eliseev
9. Sai H. Vemprala
10. Ying Sheng

Papers from Saleh Ashkboos's perspective

Ordered by how strongly the researchers Saleh Ashkboos 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. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers (2022) — 2,286 citations
• SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression (2023) — 444 citations
• SliceGPT: Compress Large Language Models by Deleting Rows and Columns (2024) — 412 citations
• QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs (2024) — 576 citations
• OPTQ: Accurate Quantization for Generative Pre-trained Transformers (2023) — 431 citations