Ali Ayub

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 12 papers; trusts 1,405 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #6,352 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 165 citations into it; #5,917 of 21,575 by trust here, #10,010 by citations
• Meta & Continual Learning — 81 citations into it; #228 of 1,041 by trust here, #635 by citations
• Representation & Self-Supervised Learning — 48 citations into it; #2,134 of 3,145 by trust here, #2,691 by citations
• Evaluation & Benchmarks — 31 citations into it
• Cognitive Neuroscience — 23 citations into it
• Model Architectures — 23 citations into it
• Optimization & Training — 23 citations into it

Whom Ali Ayub trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Alan R. Wagner
6. Yann LeCun
7. Ross B. Girshick
8. David G. Lowe
9. X. Zhang
10. Ilya Sutskever

Who trusts Ali Ayub overall

1. Alan R. Wagner
2. C. Fendley
3. Tyler L. Hayes
4. Liyuan Wang
5. Christopher Kanan
6. Chrystopher L. Nehaniv
7. Anna Kukleva
8. Gyuhak Kim
9. Haoxuan Qu
10. James Smith

Papers from Ali Ayub's perspective

Ordered by how strongly the researchers Ali Ayub 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. 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. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Cognitively-Inspired Model for Incremental Learning Using a Few Examples (2020) — 51 citations
• Few-Shot Continual Active Learning by a Robot (2022) — 36 citations
• Don’t Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot (2022) — 7 citations
• Learning Novel Objects Continually Through Curiosity (2021) — 4 citations
• EEC: Learning to Encode and Regenerate Images for Continual Learning (2021) — 63 citations
• F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning (2021) — 14 citations
• Online Learning of Objects through Curiosity-Driven Active Learning (2021) — 3 citations
• Brain-Inspired Model for Incremental Learning Using a Few Examples (2020) — 3 citations
• What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper (2020) — 14 citations
• Tell me what this is: Few-Shot Incremental Object Learning by a Robot (2020) — 34 citations
• Storing Encoded Episodes as Concepts for Continual Learning (2020) — 13 citations
• Centroid Based Concept Learning for RGB-D Indoor Scene Classification (2019) — 32 citations