Michael Ferdman

Computer Vision and Pattern Recognition · 2006–2017

A researcher in the Science corpus: 8 papers; trusts 141 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 49 citations into it; #10,476 of 21,575 by trust here, #8,831 by citations
• Electrical and Electronic Engineering — 37 citations into it; #4,893 of 16,539 by trust here, #5,345 by citations
• Hardware and Architecture — 36 citations into it; #1,513 of 1,551 by trust here, #341 by citations
• Artificial Intelligence — 19 citations into it; #16,512 of 35,345 by trust here, #12,761 by citations
• ML Systems & Hardware — 18 citations into it
• Efficiency & Compression — 10 citations into it; #763 of 1,264 by trust here, #925 by citations
• Model Architectures — 9 citations into it

Whom Michael Ferdman trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Andrew Zisserman
6. Clément Farabet
7. Yann LeCun
8. Christian Szegedy
9. Srimat T. Chakradhar
10. Ronan Collobert

Who trusts Michael Ferdman overall

1. Sparsh Mittal
2. Cheol-Ho Hong
3. Akshitha Sriraman
4. Yongming Shen
5. Junwhan Ahn
6. Duy-Thanh Nguyen
7. Ahmad Shawahna
8. Fangfei Liu
9. Md Maruf Hossain Shuvo
10. Thomas F. Wenisch

Papers from Michael Ferdman's perspective

Ordered by how strongly the researchers Michael Ferdman 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. Going deeper with convolutions
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Maximizing CNN accelerator efficiency through resource partitioning (2016) — 336 citations
• Fused-layer CNN accelerators (2016) — 668 citations
• Toward Dark Silicon in Servers (2011) — 335 citations
• Escher: A CNN Accelerator with Flexible Buffering to Minimize Off-Chip Transfer (2017) — 106 citations
• Clearing the clouds: a study of emerging scale-out workloads on modern hardware (2012) — 1,015 citations
• Reactive NUCA: near-optimal block placement and replication in distributed caches (2009) — 383 citations
• Spatial Memory Streaming (2006) — 304 citations
• SimFlex: Statistical Sampling of Computer System Simulation (2006) — 380 citations