Prem Melville

Artificial Intelligence · 2002–2009

A researcher in the Science corpus: 7 papers; trusts 47 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #6,462 of 35,345 by trust here, #9,968 by citations
• Evaluation & Benchmarks — 15 citations into it
• Statistical Learning Theory — 13 citations into it
• Natural Language Processing — 10 citations into it
• Information Systems — 7 citations into it; #6,569 of 8,771 by trust here, #5,078 by citations
• Meta & Continual Learning — 6 citations into it

Whom Prem Melville trusts overall

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

1. J. Ross Quinlan
2. Thomas G. Dietterich
3. David A. Cohn
4. Geoffrey I. Webb
5. Andrew McCallum
6. Leo Breiman
7. Raymond J. Mooney
8. Robert E. Schapire
9. Eric Bauer
10. Eric R. Ziegel

Who trusts Prem Melville overall

1. Burr Settles
2. Raymond J. Mooney
3. Charu C. Aggarwal
4. Lior Rokach
5. M. A. Ganaie
6. Devis Tuia
7. Sophie Ahrens
8. Hyung Jun Ahn
9. Panagiotis Symeonidis
10. Meng Fang

Papers from Prem Melville's perspective

Ordered by how strongly the researchers Prem Melville trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Diverse ensembles for active learning (2004) — 401 citations
• Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria (2009) — 339 citations
• Creating diversity in ensembles using artificial data (2005) — 310 citations
• Sentiment analysis of blogs by combining lexical knowledge with text classification (2009) — 587 citations
• Active Feature-Value Acquisition (2009) — 142 citations
• Constructing Diverse Classifier Ensembles using Artificial Training Examples (2003) — 311 citations
• Content-boosted collaborative filtering for improved recommendations (2002) — 1,378 citations