Mark D. Smucker
Information Systems · 2004–2021
A researcher in the Science corpus: 10 papers; trusts 40 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 43 citations into it; #278 of 8,771 by trust here, #664 by citations
- Information Retrieval & Search — 38 citations into it; #197 of 1,022 by trust here, #362 by citations
- Artificial Intelligence — 37 citations into it; #4,789 of 35,345 by trust here, #8,439 by citations
- Evaluation & Benchmarks — 32 citations into it; #689 of 3,147 by trust here, #1,817 by citations
- Natural Language Processing — 20 citations into it; #3,376 of 5,688 by trust here, #3,574 by citations
- Management Science and Operations Research — 10 citations into it
- Computer Science Applications — 8 citations into it; #1,515 of 1,855 by trust here, #1,676 by citations
- Signal Processing — 8 citations into it
Whom Mark D. Smucker trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald Metzler
- Robert Krovetz
- W. Bruce Croft
- Victor Lavrenko
- C. Burges
- Makoto Matsumoto
- Gordon V. Cormack
- Ellen M. Voorhees
- C. Clarke
- Chengxiang Zhai
Who trusts Mark D. Smucker overall
- Qingyao Ai
- Jimmy J. Lin
- Jiafeng Guo
- W. Bruce Croft
- Hamed Zamani
- Mark Sanderson
- Tetsuya Sakai
- Mariano Felice
- Yongfeng Zhang
- Jamie Callan
Papers from Mark D. Smucker's perspective
Ordered by how strongly the researchers Mark D. Smucker trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- A comparison of statistical significance tests for information retrieval evaluation (2007) — 787 citations
- UMass at TREC 2004: Novelty and HARD (2004) — 359 citations
- Evaluation Measures Based on Preference Graphs (2021) — 6 citations
- Assessing Top- Preferences (2020) — 34 citations
- Effective User Interaction for High-Recall Retrieval: Less is More (2018) — 29 citations
- A System for Efficient High-Recall Retrieval (2018) — 47 citations
- Evaluating sentence-level relevance feedback for high-recall information retrieval (2018) — 27 citations
- Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering (2017) — 10 citations
- Efficient and effective spam filtering and re-ranking for large web datasets (2010) — 355 citations
- Find-similar: similarity browsing as a search tool (2006) — 69 citations