Mohammad Havaei

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 252 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #15,735 of 21,575 by trust here, #3,747 by citations
• Artificial Intelligence — 70 citations into it
• Generative Models — 43 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Optimization & Training — 16 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Model Architectures — 10 citations into it
• Meta & Continual Learning — 8 citations into it

Whom Mohammad Havaei trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ilya Sutskever
5. Diederik P. Kingma
6. Kaiming He
7. Yann LeCun
8. Evan Shelhamer
9. Ian J. Goodfellow
10. Ruslan Salakhutdinov

Who trusts Mohammad Havaei overall

1. Mateusz Buda
2. Guotai Wang
3. Konstantinos Kamnitsas
4. Maciej A. Mazurowski
5. Wenqi Li
6. Jose Dolz
7. M. Bakator
8. David P. Helmbold
9. Yu-An Chung
10. Agisilaos Chartsias

Papers from Mohammad Havaei's perspective

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

Their papers

• Brain tumor segmentation with Deep Neural Networks (2015) — 3,119 citations
• Dual Adversarial Inference for Text-to-Image Synthesis (2019) — 40 citations
• Learnable Explicit Density for Continuous Latent Space and Variational Inference (2017) — 28 citations
• Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay (2020) — 34 citations
• HeMIS: Hetero-Modal Image Segmentation (2016) — 355 citations
• ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI (2016) — 525 citations