Daniel Jiwoong Im

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 7 papers; trusts 372 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 78 citations into it; #5,619 of 21,575 by trust here, #4,955 by citations
• Artificial Intelligence — 68 citations into it; #17,503 of 35,345 by trust here, #18,586 by citations
• Generative Models — 53 citations into it; #589 of 1,956 by trust here, #616 by citations
• Optimization & Training — 40 citations into it; #2,169 of 2,601 by trust here, #2,052 by citations
• Probabilistic & Bayesian Methods — 24 citations into it
• Model Architectures — 21 citations into it
• Statistical and Nonlinear Physics — 17 citations into it; #8,657 of 10,127 by trust here, #6,421 by citations
• Representation & Self-Supervised Learning — 11 citations into it

Whom Daniel Jiwoong Im trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Yann LeCun
5. Alex Krizhevsky
6. Ian J. Goodfellow
7. Kaiming He
8. L. Eon Bottou
9. Radford M. Neal
10. Ruslan Salakhutdinov

Who trusts Daniel Jiwoong Im overall

1. Leslie N. Smith
2. Timothée Lesort
3. Yongjun Hong
4. He Ma
5. Nicholay Topin
6. Charles H. Martin
7. Hao Li
8. Graham W. Taylor
9. Ryan Webster
10. Jiuxiang Gu

Papers from Daniel Jiwoong Im's perspective

Ordered by how strongly the researchers Daniel Jiwoong Im 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generating images with recurrent adversarial networks (2016) — 227 citations
• Quantitatively Evaluating GANs With Divergences Proposed for Training (2018) — 73 citations
• An empirical analysis of the optimization of deep network loss surfaces (2016) — 66 citations
• Denoising Criterion for Variational Auto-Encoding Framework (2015) — 224 citations
• Generative Adversarial Parallelization (2016) — 40 citations
• Stochastic Neighbor Embedding under f-divergences (2018) — 23 citations
• An Empirical Analysis of Deep Network Loss Surfaces (2016) — 48 citations