Massoud Pedram

Computer Vision and Pattern Recognition · Proceedings of the IEEE · 1999–2019

A researcher in the Science corpus: 9 papers; trusts 262 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it; #11,613 of 21,575 by trust here, #16,367 by citations
• Artificial Intelligence — 29 citations into it
• Electrical and Electronic Engineering — 16 citations into it
• Model Architectures — 10 citations into it
• Hardware and Architecture — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Optimization & Training — 6 citations into it
• Reinforcement Learning — 6 citations into it

Whom Massoud Pedram trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Yoshua Bengio
6. Ilya Sutskever
7. Christian Szegedy
8. Yann LeCun
9. Diederik P. Kingma
10. Ruslan Salakhutdinov

Who trusts Massoud Pedram overall

1. Young Geun Kim
2. Gyuhae Park
3. Carole-Jean Wu
4. Luca Benini
5. T. Rosing

Papers from Massoud Pedram's perspective

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

Their papers

• Coarse2Fine: a two-stage training method for fine-grained visual classification (2019) — 13 citations
• Fine-grained dynamic voltage and frequency scaling for precise energy and performance trade-off based on the ratio of off-chip access to on-chip computation times (2004) — 332 citations
• Deriving a near-optimal power management policy using model-free reinforcement learning and Bayesian classification (2011) — 64 citations
• Gradient Agreement as an Optimization Objective for Meta-Learning (2018) — 16 citations
• JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services (2018) — 305 citations
• Thermal Modeling, Analysis, and Management in VLSI Circuits: Principles and Methods (2006) — 497 citations
• Standby and Active Leakage Current Control and Minimization in CMOS VLSI Circuits (2005) — 304 citations
• Leakage current reduction in CMOS VLSI circuits by input vector control (2004) — 302 citations
• Dynamic power management based on continuous-time Markov decision processes (1999)