Swagath Venkataramani

Computer Vision and Pattern Recognition · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 168 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 81 citations into it; #11,551 of 21,575 by trust here, #6,281 by citations
• Artificial Intelligence — 75 citations into it; #18,335 of 35,345 by trust here, #8,810 by citations
• Efficiency & Compression — 33 citations into it
• Model Architectures — 30 citations into it
• Optimization & Training — 28 citations into it
• Electrical and Electronic Engineering — 16 citations into it; #4,096 of 16,539 by trust here, #9,210 by citations
• Hardware and Architecture — 12 citations into it
• Signal Processing — 11 citations into it

Whom Swagath Venkataramani 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. Hadi Esmaeilzadeh
6. Yann LeCun
7. Karen Simonyan
8. Ilya Sutskever
9. Diederik P. Kingma
10. X. Zhang

Who trusts Swagath Venkataramani overall

1. Sparsh Mittal
2. M. Mohammadi
3. Soheil Hashemi
4. Kaushik Roy
5. Bert Moons
6. Isha Garg
7. Xiaojing Xu
8. Priyadarshini Panda
9. Ala Al-Fuqaha
10. Simone Scardapane

Papers from Swagath Venkataramani's perspective

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

Their papers

• PACT: Parameterized Clipping Activation for Quantized Neural Networks (2018) — 1,126 citations
• AxNN: Energy-efficient neuromorphic systems using approximate computing (2014) — 258 citations
• SALSA: Systematic logic synthesis of approximate circuits (2012) — 356 citations
• Efficient embedded learning for IoT devices (2016) — 36 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
• Scalable-effort classifiers for energy-efficient machine learning (2015) — 88 citations