Songhwai Oh

Computer Vision and Pattern Recognition · 2004–2019

A researcher in the Science corpus: 6 papers; trusts 76 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 27 citations into it; #15,300 of 21,575 by trust here, #19,766 by citations
• Artificial Intelligence — 26 citations into it; #15,347 of 35,345 by trust here, #18,186 by citations
• Reinforcement Learning — 19 citations into it
• Computer Networks and Communications — 8 citations into it
• Management Science and Operations Research — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Songhwai Oh trusts overall

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

1. Reza Olfati-Saber
2. Stuart Geman
3. Simon Justin Julier
4. Marc G. Bellemare
5. Leslie G. Valiant
6. Will Dabney
7. Arnaud Doucet
8. Iain Murray
9. Tamer Basar
10. Dirk Schulz

Who trusts Songhwai Oh overall

1. Jason L. Williams
2. Ben Benfold
3. B. Vo
4. Yu Xiang
5. Qi Chu
6. Seyed Hamid Rezatofighi
7. Ian David Reid
8. Hung Gia Hoang
9. Anton Milan
10. Alexandre Alahi

Papers from Songhwai Oh's perspective

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

Their papers

• Markov chain Monte Carlo data association for general multiple-target tracking problems (2004) — 302 citations
• Distributional Deep Reinforcement Learning with a Mixture of Gaussians (2019) — 34 citations
• Individualness and Determinantal Point Processes for Pedestrian Detection (2016) — 37 citations
• Efficient $l_{1}$ -Norm-Based Low-Rank Matrix Approximations for Large-Scale Problems Using Alternating Rectified Gradient Method (2015) — 46 citations
• Distributed learning and cooperative control for multi-agent systems (2009) — 233 citations
• Markov Chain Monte Carlo Data Association for Multi-Target Tracking (2009) — 346 citations