Vijayalakshmi Srinivasan

Artificial Intelligence · 2002–2020

A researcher in the Science corpus: 8 papers; trusts 130 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #18,336 of 35,345 by trust here, #9,641 by citations
• Computer Vision and Pattern Recognition — 66 citations into it; #11,552 of 21,575 by trust here, #6,795 by citations
• Efficiency & Compression — 32 citations into it
• Model Architectures — 28 citations into it
• Optimization & Training — 23 citations into it
• Signal Processing — 9 citations into it
• Media Technology — 5 citations into it

Whom Vijayalakshmi Srinivasan trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. X. Zhang
6. Diederik P. Kingma
7. Karen Simonyan
8. Shaoqing Ren
9. Ilya Sutskever
10. Yann LeCun

Who trusts Vijayalakshmi Srinivasan overall

1. Michael Zhang
2. Onur Mutlu
3. Luiz E. Ramos
4. Jian Xu
5. Sangyeun Cho
6. Krste Asanovic
7. Adrian Sampson
8. H. Peter Hofstee

Papers from Vijayalakshmi Srinivasan's perspective

Ordered by how strongly the researchers Vijayalakshmi Srinivasan 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. Gradient-based learning applied to document recognition
7. ImageNet Large Scale Visual Recognition Challenge
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• PACT: Parameterized Clipping Activation for Quantized Neural Networks (2018) — 1,126 citations
• Scalable high performance main memory system using phase-change memory technology (2009) — 1,213 citations
• Enhancing lifetime and security of PCM-based Main Memory with Start-Gap Wear Leveling (2009) — 759 citations
• Microarchitectural techniques for power gating of execution units (2004) — 439 citations
• Optimizing pipelines for power and performance (2002) — 160 citations
• Ultra-Low Precision 4-bit Training of Deep Neural Networks (2020) — 217 citations
• Accurate and Efficient 2-bit Quantized Neural Networks (2019) — 172 citations
• Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks (2019) — 255 citations