Asela Gunawardana

Information Systems · 2005–2011

A researcher in the Science corpus: 5 papers; trusts 44 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 42 citations into it; #421 of 8,771 by trust here, #1,307 by citations
• Artificial Intelligence — 34 citations into it; #9,037 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #9,176 of 21,575 by trust here, #16,367 by citations
• Management Science and Operations Research — 12 citations into it; #1,449 of 11,946 by trust here, #2,957 by citations
• Natural Language Processing — 12 citations into it
• Signal Processing — 11 citations into it; #2,691 of 7,572 by trust here, #3,198 by citations
• Evaluation & Benchmarks — 9 citations into it
• Recommender Systems — 8 citations into it

Whom Asela Gunawardana trusts overall

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

1. Ruslan Salakhutdinov
2. Andrew McCallum
3. Aapo Hyvärinen
4. Andrew I. Schein
5. Paul Resnick
6. Robin D. Burke
7. M. Stone
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Michael Collins

Who trusts Asela Gunawardana overall

1. Li Deng
2. Alan Said
3. Yongfeng Zhang
4. Yue Shi
5. Dong Yu
6. Harald Steck
7. Geoffry Zweig
8. Michael D. Ekstrand
9. Louis-philippe Morency
10. Georg Heigold

Papers from Asela Gunawardana's perspective

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

Their papers

• Hidden conditional random fields for phone classification (2005) — 366 citations
• A unified approach to building hybrid recommender systems (2009) — 178 citations
• Evaluating Recommendation Systems (2011) — 1,451 citations
• Efficient Subsampling for Training Complex Language Models (2011) — 28 citations
• A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (2009) — 715 citations