Thalaiyasingam Ajanthan

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 215 researchers and is trusted by 337 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #1,626 of 35,345 by trust here, #1,691 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #2,301 of 21,575 by trust here, #1,648 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #345 of 3,145 by trust here, #367 by citations
• Optimization & Training — 27 citations into it
• Efficiency & Compression — 25 citations into it; #323 of 1,264 by trust here, #573 by citations
• Reinforcement Learning — 20 citations into it
• Meta & Continual Learning — 15 citations into it; #136 of 1,041 by trust here, #56 by citations
• Model Architectures — 15 citations into it

Whom Thalaiyasingam Ajanthan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Diederik P. Kingma
5. Yoshua Bengio
6. Kaiming He
7. Ilya Sutskever
8. Karen Simonyan
9. Marc'Aurelio Ranzato
10. Jimmy Ba

Who trusts Thalaiyasingam Ajanthan overall

1. Arslan Chaudhry
2. Rahaf Aljundi
3. Zheda Mai
4. Quang Pham
5. Lucas Caccia
6. Jiahui Yu
7. Hongjoon Ahn
8. Seyed Iman Mirzadeh
9. Marc Masana
10. Ali Ayub

Papers from Thalaiyasingam Ajanthan's perspective

Ordered by how strongly the researchers Thalaiyasingam Ajanthan 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence (2018) — 1,405 citations
• SNIP: Single-shot Network Pruning based on Connection Sensitivity (2018) — 1,499 citations
• Continual Learning with Tiny Episodic Memories (2019) — 496 citations
• A Signal Propagation Perspective for Pruning Neural Networks at Initialization (2019) — 174 citations
• On Tiny Episodic Memories in Continual Learning (2019) — 896 citations