Mohammad Rostami

Artificial Intelligence · with Kolouri · 2016–2022

A researcher in the Science corpus: 17 papers; trusts 2,205 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 435 citations into it; #6,514 of 35,345 by trust here, #6,848 by citations
• Computer Vision and Pattern Recognition — 365 citations into it; #5,380 of 21,575 by trust here, #7,657 by citations
• Representation & Self-Supervised Learning — 202 citations into it
• Computational Mechanics — 78 citations into it
• Natural Language Processing — 72 citations into it
• Reinforcement Learning — 48 citations into it; #985 of 2,242 by trust here, #1,609 by citations
• Meta & Continual Learning — 45 citations into it; #190 of 1,041 by trust here, #361 by citations
• Radiology, Nuclear Medicine and Imaging — 45 citations into it

Whom Mohammad Rostami trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Radford M. Neal
10. Ross B. Girshick

Who trusts Mohammad Rostami overall

1. Serban Stan
2. Zixuan Ke
3. David Isele
4. Gyuhak Kim
5. Liyuan Wang
6. Bing Liu
7. Soheil Kolouri
8. Aram Galstyan
9. Eric Eaton
10. Leonidas Spinoulas

Papers from Mohammad Rostami's perspective

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

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

Their papers

• Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer (2017) — 42 citations
• Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021) — 47 citations
• Explainability Methods for Graph Convolutional Neural Networks (2019) — 586 citations
• CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks (2022) — 94 citations
• Detection and Continual Learning of Novel Face Presentation Attacks (2021) — 56 citations
• Unsupervised Model Adaptation for Continual Semantic Segmentation (2020) — 71 citations
• Sequential Unsupervised Domain Adaptation through Prototypical Distributions (2020) — 7 citations
• SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning (2019) — 61 citations
• Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment (2019) — 24 citations
• Generative Continual Concept Learning (2019) — 52 citations
• Deep Transfer Learning for Few-Shot SAR Image Classification (2019) — 199 citations
• Learning Transferable Knowledge Through Embedding Spaces (2019) — 9 citations
• Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (2019) — 83 citations
• A crowdsourcing triage algorithm for geopolitical event forecasting (2018) — 37 citations
• Joint Dictionaries for Zero-Shot Learning (2017) — 26 citations
• Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017) — 39 citations
• Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning (2016) — 93 citations