Andrew Trotman
Information Systems · 2005–2020
A researcher in the Science corpus: 9 papers; trusts 24 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 36 citations into it; #1,227 of 8,771 by trust here, #2,667 by citations
- Information Retrieval & Search — 33 citations into it; #195 of 1,022 by trust here, #471 by citations
- Artificial Intelligence — 29 citations into it; #16,385 of 35,345 by trust here, #19,873 by citations
- Computer Networks and Communications — 18 citations into it; #2,875 of 9,886 by trust here, #5,961 by citations
- Management Science and Operations Research — 17 citations into it
- Evaluation & Benchmarks — 10 citations into it
- Reinforcement Learning — 10 citations into it
- Computer Vision and Pattern Recognition — 6 citations into it
Whom Andrew Trotman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Justin Zobel
- Jimmy J. Lin
- Xiangfei Jia
- Matt Crane
- Peter Auer
- Richard S. Sutton
- Thorsten Joachims
- Daniel Lemire
- Masrour Zoghi
- Donald Metzler
Who trusts Andrew Trotman overall
- Jimmy J. Lin
- Joel M. Mackenzie
- Matt Crane
- Chris Kamphuis
- J. Shane Culpepper
- Peilin Yang
- C. Clarke
- Guanjie Zheng
- A. D. Vries
- Hui Fang
Papers from Andrew Trotman's perspective
Ordered by how strongly the researchers Andrew Trotman trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Toward Reproducible Baselines: The Open-Source IR Reproducibility Challenge (2016) — 111 citations
- A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation (2017) — 68 citations
- Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format (2020) — 46 citations
- Micro‐ and macro‐optimizations of SaaT search (2019) — 17 citations
- Anytime Ranking for Impact-Ordered Indexes (2015) — 63 citations
- Improvements to BM25 and Language Models Examined (2014) — 278 citations
- Towards an Efficient and Effective Search Engine (2012) — 51 citations
- IR Evaluation Using Multiple Assessors per Topic (2007)
- Learning to Rank (2005) — 66 citations