B. Vo

Artificial Intelligence · 2003–2017

A researcher in the Science corpus: 19 papers; trusts 115 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #2,773 of 35,345 by trust here, #5,252 by citations
• Statistics and Probability — 42 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it; #928 of 1,525 by trust here, #1,176 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #10,548 of 21,575 by trust here, #16,367 by citations
• Computer Networks and Communications — 27 citations into it; #393 of 9,886 by trust here, #943 by citations
• Applied Mathematics — 24 citations into it
• Management Science and Operations Research — 23 citations into it
• Aerospace Engineering — 12 citations into it; #4,612 of 6,421 by trust here, #4,388 by citations

Whom B. Vo trusts overall

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

1. Ronald P. S. Mahler
2. Arnaud Doucet
3. Radford M. Neal
4. Ba-Ngu Vo
5. Dominic Schuhmacher
6. Antonio Cantoni
7. Simon Justin Julier
8. Daryl J. Daley
9. Sumeetpal S. Singh
10. Carine Hue

Who trusts B. Vo overall

1. Jason L. Williams
2. Karl Granström
3. Ba-Ngu Vo
4. B. Ristic
5. Dominic Schuhmacher
6. Stephan Reuter
7. Dinh Q. Phung
8. Hung Gia Hoang
9. Marcus Baum
10. Bahador Khaleghi

Papers from B. Vo's perspective

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

Their papers

• Model-based classification and novelty detection for point pattern data (2016) — 14 citations
• Model-Based Multiple Instance Learning (2017) — 5 citations
• Clustering for point pattern data (2017) — 13 citations
• A Random-Finite-Set Approach to Bayesian SLAM (2011) — 304 citations
• An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016) — 354 citations
• The Labeled Multi-Bernoulli Filter (2014) — 775 citations
• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013) — 705 citations
• Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013) — 927 citations
• A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013) — 296 citations
• Adaptive Target Birth Intensity for PHD and CPHD Filters (2012) — 303 citations
• A Metric for Performance Evaluation of Multi-Target Tracking Algorithms (2011) — 333 citations
• Joint Detection and Estimation of Multiple Objects From Image Observations (2010) — 380 citations
• The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations (2009) — 878 citations
• On performance evaluation of multi-object filters (2008) — 426 citations
• A Consistent Metric for Performance Evaluation of Multi-Object Filters (2008) — 2,077 citations
• Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter (2007) — 851 citations
• The Gaussian Mixture Probability Hypothesis Density Filter (2006) — 2,018 citations
• Sequential Monte Carlo methods for multitarget filtering with random finite sets (2005) — 1,280 citations
• Sequential monte carlo implementation of the phd filter for multi-target tracking (2003) — 337 citations