Moin Nabi

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 11 papers; trusts 697 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #4,367 of 35,345 by trust here, #3,261 by citations
• Computer Vision and Pattern Recognition — 226 citations into it; #6,124 of 21,575 by trust here, #4,174 by citations
• Representation & Self-Supervised Learning — 90 citations into it
• Meta & Continual Learning — 60 citations into it; #263 of 1,041 by trust here, #172 by citations
• Natural Language Processing — 30 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Optimization & Training — 20 citations into it
• Efficiency & Compression — 16 citations into it

Whom Moin Nabi trusts overall

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

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

Who trusts Moin Nabi overall

1. Lichao Sun
2. Enrico Fini
3. Gyuhak Kim
4. Luping Wang
5. Tribhuvanesh Orekondy
6. Congzheng Song
7. Stephanie L. Hyland
8. Liyuan Wang
9. Ali Ayub
10. Radu Tudor Ionescu

Papers from Moin Nabi's perspective

Ordered by how strongly the researchers Moin Nabi 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (2019) — 356 citations
• FOIL it! Find One mismatch between Image and Language caption (2017) — 162 citations
• Online Continual Learning under Extreme Memory Constraints (2020) — 74 citations
• Abnormal event detection in videos using generative adversarial nets (2017) — 482 citations
• Differentially Private Federated Learning: A Client Level Perspective (2017) — 1,680 citations
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning (2021) — 172 citations
• A Unified Objective for Novel Class Discovery (2021) — 266 citations
• Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (2022) — 97 citations
• A soft nearest-neighbor framework for continual semi-supervised learning (2022) — 26 citations
• Contrastive Self-Supervised Learning for Commonsense Reasoning (2020) — 64 citations
• Learning to remember: Dynamic Generative Memory for Continual Learning (2018)