Soheil Kolouri

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 18 papers; trusts 1,045 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 256 citations into it; #5,430 of 35,345 by trust here, #4,888 by citations
• Computer Vision and Pattern Recognition — 249 citations into it; #4,770 of 21,575 by trust here, #4,317 by citations
• Representation & Self-Supervised Learning — 113 citations into it
• Cognitive Neuroscience — 55 citations into it
• Computational Mechanics — 50 citations into it
• Optimization & Training — 42 citations into it
• Meta & Continual Learning — 38 citations into it; #202 of 1,041 by trust here, #456 by citations
• Cellular and Molecular Neuroscience — 37 citations into it

Whom Soheil Kolouri trusts overall

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

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

Who trusts Soheil Kolouri overall

1. Mohammad Rostami
2. Zixuan Ke
3. Serban Stan
4. David Isele
5. Eric Eaton
6. Mehrdad Farajtabar
7. Seyed Iman Mirzadeh
8. G. Peyr'e
9. Marco Cuturi
10. François-Pierre Paty

Papers from Soheil Kolouri's perspective

Ordered by how strongly the researchers Soheil Kolouri 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Image to Image Translation for Domain Adaptation (2017) — 561 citations
• Attention-Based Structural-Plasticity (2019) — 12 citations
• Explaining Distributed Neural Activations via Unsupervised Learning (2017) — 8 citations
• Optimal Mass Transport: Signal processing and machine-learning applications (2017) — 477 citations
• Explainability Methods for Graph Convolutional Neural Networks (2019) — 586 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• Is Multi-Task Learning an Upper Bound for Continual Learning? (2022) — 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
• Attention-Based Structural-Plasticity (2019)
• Generative Continual Concept Learning (2019) — 52 citations
• Generalized Sliced Wasserstein Distances (2019) — 389 citations
• Deep Transfer Learning for Few-Shot SAR Image Classification (2019) — 199 citations
• Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (2019) — 83 citations
• Joint Dictionaries for Zero-Shot Learning (2017) — 26 citations
• Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017) — 39 citations
• Sliced Wasserstein Distance for Learning Gaussian Mixture Models (2017) — 143 citations
• Sliced Wasserstein Kernels for Probability Distributions (2015) — 182 citations