Raghuraman Krishnamoorthi

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 9 papers; trusts 451 researchers and is trusted by 131 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 211 citations into it; #1,694 of 35,345 by trust here, #5,565 by citations
• Computer Vision and Pattern Recognition — 191 citations into it; #2,689 of 21,575 by trust here, #5,620 by citations
• Efficiency & Compression — 57 citations into it
• Model Architectures — 27 citations into it; #149 of 2,278 by trust here, #987 by citations
• Electrical and Electronic Engineering — 20 citations into it
• Optimization & Training — 19 citations into it; #207 of 2,601 by trust here, #930 by citations
• Signal Processing — 5 citations into it

Whom Raghuraman Krishnamoorthi trusts overall

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

1. Kaiming He
2. Christian Szegedy
3. Sergey Ioffe
4. X. Zhang
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Alex Krizhevsky
8. Shaoqing Ren
9. Andrew G. Howard
10. Yihui He

Who trusts Raghuraman Krishnamoorthi overall

1. Markus Nagel
2. Gaurav Menghani
3. Tijmen Blankevoort
4. Zhen Dong
5. Zi-Mei Yao
6. Yelysei Bondarenko
7. Angela Fan
8. Tim Dettmers
9. Alexandre D'efossez
10. Ying Wang

Papers from Raghuraman Krishnamoorthi's perspective

Ordered by how strongly the researchers Raghuraman Krishnamoorthi 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Quantizing deep convolutional networks for efficient inference: A whitepaper (2018) — 1,281 citations
• MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning (2023) — 726 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• SpinQuant: LLM quantization with learned rotations (2024) — 438 citations
• LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding (2024) — 321 citations
• EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything (2023) — 303 citations
• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2023) — 398 citations
• Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale (2021) — 14 citations
• This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.