Geethan Karunaratne

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 60 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it
• Electrical and Electronic Engineering — 50 citations into it; #13,466 of 16,539 by trust here, #11,313 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Natural Language Processing — 10 citations into it
• Hardware and Architecture — 5 citations into it

Whom Geethan Karunaratne trusts overall

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

1. Pentti Kanerva
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Graves
6. Diederik P. Kingma
7. Kaiming He
8. Ruslan Salakhutdinov
9. Yann LeCun
10. Sepp Hochreiter

Who trusts Geethan Karunaratne overall

1. Michael Hersche
2. Abu Sebastian
3. Leonardo Ravaglia
4. Luca Benini
5. Keon-Hee Park

Papers from Geethan Karunaratne's perspective

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

Their papers

• Constrained Few-shot Class-incremental Learning (2022) — 204 citations
• Robust high-dimensional memory-augmented neural networks (2020) — 123 citations
• In-memory hyperdimensional computing (2019) — 305 citations
• Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks (2022) — 10 citations
• A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference (2022) — 314 citations