Anand Raghunathan

Artificial Intelligence · 2003–2020

A researcher in the Science corpus: 15 papers; trusts 143 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #24,066 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Electrical and Electronic Engineering — 26 citations into it; #2,531 of 16,539 by trust here, #6,558 by citations
• Efficiency & Compression — 24 citations into it
• Hardware and Architecture — 24 citations into it; #1,426 of 1,551 by trust here, #757 by citations
• Computer Networks and Communications — 20 citations into it
• Safety, Robustness & Interpretability — 15 citations into it; #1,572 of 2,251 by trust here, #1,512 by citations
• Signal Processing — 14 citations into it

Whom Anand Raghunathan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Yoshua Bengio
6. Gregory K. Wallace
7. Diederik P. Kingma
8. Ronan Collobert
9. Jerome H. Friedman
10. Ilya Sutskever

Who trusts Anand Raghunathan overall

1. Sparsh Mittal
2. Swagath Venkataramani
3. Kaushik Roy
4. M. Mohammadi
5. Nicolas Papernot
6. Priyadarshini Panda
7. Soheil Hashemi
8. Abdulmalik A. Humayed
9. Simone Scardapane
10. Chi Lo

Papers from Anand Raghunathan's perspective

Ordered by how strongly the researchers Anand Raghunathan 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Learning Multiple Layers of Features from Tiny Images
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015) — 347 citations
• AxNN: Energy-efficient neuromorphic systems using approximate computing (2014) — 258 citations
• SALSA: Systematic logic synthesis of approximate circuits (2012) — 356 citations
• Analysis and characterization of inherent application resilience for approximate computing (2013) — 532 citations
• IMPACT: IMPrecise adders for low-power approximate computing (2011) — 483 citations
• Efficient embedded learning for IoT devices (2016) — 36 citations
• Low-Power Digital Signal Processing Using Approximate Adders (2013) — 821 citations
• Hijacking an insulin pump: Security attacks and defenses for a diabetes therapy system (2011) — 355 citations
• Gradual Channel Pruning While Training Using Feature Relevance Scores for Convolutional Neural Networks (2020) — 29 citations
• Scalable-effort classifiers for energy-efficient machine learning (2015) — 88 citations
• SPINDLE: SPINtronic Deep Learning Engine for large-scale neuromorphic computing (2014) — 86 citations
• A study of the energy consumption characteristics of cryptographic algorithms and security protocols (2006) — 474 citations
• Security as a new dimension in embedded system design (2004) — 444 citations
• Security in embedded systems: Design challenges (2004) — 536 citations
• Analyzing the energy consumption of security protocols (2003) — 317 citations