Hamid R. Tizhoosh
Artificial Intelligence · 2005–2021
A researcher in the Science corpus: 12 papers; trusts 109 researchers and is trusted by 59 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 80 citations into it; #3,585 of 35,345 by trust here, #6,569 by citations
- Computer Vision and Pattern Recognition — 33 citations into it; #5,769 of 21,575 by trust here, #11,721 by citations
- Computational Theory and Mathematics — 25 citations into it; #1,526 of 12,182 by trust here, #1,748 by citations
- Reinforcement Learning — 11 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Numerical Analysis — 6 citations into it
- Evaluation & Benchmarks — 5 citations into it
Whom Hamid R. Tizhoosh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ben J. A. Kröse
- David E. Goldberg
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Rob Fergus
- Bülent Sankur
- Richard S. Sutton
- Godfrey C. Onwubolu
- Honglak Lee
Who trusts Hamid R. Tizhoosh overall
- Shahryar Rahnamayan
- Sedigheh Mahdavi
- Magdy M. A. Salama
- Maziar Yazdani
- Ioannis K. Vlachos
- Hui Wang
- Qing Xu
- Mohammad Ebrahim Shiri
- Ferrante Neri
- Ali Akbar Movassagh
Papers from Hamid R. Tizhoosh's perspective
Ordered by how strongly the researchers Hamid R. Tizhoosh trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Opposition-Based Differential Evolution (2008) — 1,586 citations
- Quasi-oppositional Differential Evolution (2007) — 350 citations
- A novel population initialization method for accelerating evolutionary algorithms (2007) — 375 citations
- Federated learning and differential privacy for medical image analysis (2021) — 429 citations
- Artificial Intelligence and Digital Pathology: Challenges and Opportunities (2018) — 470 citations
- Autoencoding the retrieval relevance of medical images (2015) — 41 citations
- Opposition versus randomness in soft computing techniques (2008) — 340 citations
- Application of reinforcement learning for segmentation of transrectal ultrasound images (2008) — 283 citations
- Application of Opposition-Based Reinforcement Learning in Image Segmentation (2007) — 48 citations
- A Reinforcement Learning Framework for Medical Image Segmentation (2006) — 76 citations
- Opposition-Based Learning: A New Scheme for Machine Intelligence (2005) — 2,080 citations
- Image thresholding using type II fuzzy sets (2005) — 322 citations