Brian McFee
Computer Vision and Pattern Recognition · 2010–2019
A researcher in the Science corpus: 8 papers; trusts 83 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 44 citations into it; #9,013 of 21,575 by trust here, #7,249 by citations
- Artificial Intelligence — 34 citations into it; #27,167 of 35,345 by trust here, #12,580 by citations
- Signal Processing — 33 citations into it; #2,638 of 7,572 by trust here, #1,351 by citations
- Evaluation & Benchmarks — 13 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Optimization & Training — 8 citations into it
- Information Systems — 6 citations into it
Whom Brian McFee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thorsten Joachims
- Matthias Mauch
- Kilian Q. Weinberger
- D. Ellis
- Justin Salamon
- Geoffrey E. Hinton
- Ioannis Tsochantaridis
- Alex Krizhevsky
- Jaka Demsar
- Eric J. Humphrey
Who trusts Brian McFee overall
- Brian Kulis
- Hyunsin Park
- Tong Xiao
- Andros Tjandra
- Justin Salamon
- Aurélien Bellet
- Vincent Lostanlen
- Minz Won
- Ruohan Gao
- Aäron van den Oord
Papers from Brian McFee's perspective
Ordered by how strongly the researchers Brian McFee 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- librosa: Audio and Music Signal Analysis in Python (2015) — 3,082 citations
- From region similarity to category discovery (2011) — 15 citations
- Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research (2019) — 53 citations
- Adaptive Pooling Operators for Weakly Labeled Sound Event Detection (2018) — 161 citations
- Lasagne: First release. (2015)
- A Software Framework for Musical Data Augmentation (2015) — 144 citations
- MIR_EVAL: A Transparent Implementation of Common MIR Metrics (2014) — 639 citations
- Metric Learning to Rank (2010) — 367 citations