Pramuditha Perera

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 267 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #8,881 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 47 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Media Technology — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Generative Models — 7 citations into it
• Computer Networks and Communications — 5 citations into it
• Signal Processing — 5 citations into it

Whom Pramuditha Perera trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Pascal Vincent
7. Ilya Sutskever
8. Jia Deng
9. Kaiming He
10. Peter N. Belhumeur

Who trusts Pramuditha Perera overall

1. Paul Bergmann
2. Poojan Oza
3. Mengyu Wang
4. Hanqiu Deng
5. Wenpeng Hu
6. Jihun Yi
7. Kilian Batzner
8. Tal Reiss
9. Lukas Ruff
10. Ryota Yoshihashi

Papers from Pramuditha Perera's perspective

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

• OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations (2019) — 596 citations
• Generative-Discriminative Feature Representations for Open-Set Recognition (2020) — 220 citations
• Learning Deep Features for One-Class Classification (2018) — 411 citations