Hadi Esmaeilzadeh

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 14 papers; trusts 231 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 294 citations into it
• Computer Vision and Pattern Recognition — 166 citations into it
• Electrical and Electronic Engineering — 114 citations into it; #3,760 of 16,539 by trust here, #5,046 by citations
• Privacy & Security — 88 citations into it
• Hardware and Architecture — 72 citations into it; #394 of 1,551 by trust here, #123 by citations
• Safety, Robustness & Interpretability — 54 citations into it
• Computer Networks and Communications — 48 citations into it
• Efficiency & Compression — 45 citations into it

Whom Hadi Esmaeilzadeh trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Diederik P. Kingma
5. Yoshua Bengio
6. Cynthia Dwork
7. Ilya Sutskever
8. Naveen Muralimanohar
9. Kaiming He
10. Mark D. Hill

Who trusts Hadi Esmaeilzadeh overall

1. Sparsh Mittal
2. Swagath Venkataramani
3. Adrian Sampson
4. Qiang Xu
5. Kaushik Roy
6. Hardik Sharma
7. Yujun Yan
8. Anand Raghunathan
9. Luis Ceze
10. Todd Mytkowicz

Papers from Hadi Esmaeilzadeh's perspective

Ordered by how strongly the researchers Hadi Esmaeilzadeh trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network (2017) — 561 citations
• From high-level deep neural models to FPGAs (2016) — 500 citations
• SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks (2018) — 168 citations
• A reconfigurable fabric for accelerating large-scale datacenter services (2014) — 1,234 citations
• Shredder: Learning Noise Distributions to Protect Inference Privacy (2019) — 113 citations
• ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks (2020) — 61 citations
• Architecture support for disciplined approximate programming (2012) — 461 citations
• Neural Acceleration for General-Purpose Approximate Programs (2012) — 604 citations
• Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks (2020) — 143 citations
• Privacy in Deep Learning: A Survey (2020) — 153 citations
• A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference (2020) — 13 citations
• Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019) — 12 citations
• ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks (2018)
• Dark silicon and the end of multicore scaling (2011) — 2,197 citations