A. Gholami

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 23 papers; trusts 1,045 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 669 citations into it; #4,614 of 35,345 by trust here, #2,327 by citations
• Computer Vision and Pattern Recognition — 544 citations into it; #4,182 of 21,575 by trust here, #1,918 by citations
• Efficiency & Compression — 366 citations into it; #221 of 1,264 by trust here, #161 by citations
• Optimization & Training — 185 citations into it; #1,498 of 2,601 by trust here, #804 by citations
• Model Architectures — 183 citations into it
• Large Language Models — 123 citations into it
• Natural Language Processing — 107 citations into it
• Electrical and Electronic Engineering — 64 citations into it; #14,422 of 16,539 by trust here, #8,489 by citations

Whom A. Gholami trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Diederik P. Kingma
8. Christian Szegedy
9. James Martens
10. Ross B. Girshick

Who trusts A. Gholami overall

1. Zi-Mei Yao
2. Zhen Dong
3. Sehoon Kim
4. Elias Frantar
5. Kurt Keutzer
6. Michael W. Mahoney
7. Sheng Shen
8. Tal Ben-Nun
9. Yaohui Cai
10. Coleman Hooper

Papers from A. Gholami's perspective

Ordered by how strongly the researchers A. Gholami 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT (2019) — 675 citations
• Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization (2019) — 244 citations
• Hessian-based Analysis of Large Batch Training and Robustness to Adversaries (2018) — 194 citations
• ZeroQ: A Novel Zero Shot Quantization Framework (2020) — 496 citations
• HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision (2019) — 686 citations
• On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent (2018) — 82 citations
• PyHessian: Neural Networks Through the Lens of the Hessian (2019) — 401 citations
• SqueezeNext: Hardware-Aware Neural Network Design (2018) — 329 citations
• Learned Token Pruning for Transformers (2021) — 225 citations
• Integrated Model, Batch, and Domain Parallelism in Training Neural Networks (2017) — 88 citations
• I-BERT: Integer-only BERT Quantization (2021) — 459 citations
• A Survey of Quantization Methods for Efficient Neural Network Inference (2021) — 1,633 citations
• HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks (2019) — 394 citations
• ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning (2020) — 383 citations
• AI and Memory Wall (2024) — 374 citations
• KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization (2024) — 589 citations
• SqueezeLLM: Dense-and-Sparse Quantization (2023) — 345 citations
• Big Little Transformer Decoder (2023) — 24 citations
• Characterizing possible failure modes in physics-informed neural networks (2021) — 1,222 citations
• HAWQV3: Dyadic Neural Network Quantization (2020) — 93 citations