Abdul Fatir Ansari

Computer Vision and Pattern Recognition · 2018–2024

A researcher in the Science corpus: 3 papers; trusts 159 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 44 citations into it; #16,446 of 21,575 by trust here, #18,782 by citations
• Generative Models — 37 citations into it; #1,557 of 1,956 by trust here, #1,875 by citations
• Artificial Intelligence — 34 citations into it; #26,868 of 35,345 by trust here, #25,809 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #7,384 of 10,127 by trust here, #8,564 by citations
• Optimization & Training — 5 citations into it

Whom Abdul Fatir Ansari trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ian J. Goodfellow
6. Yann LeCun
7. Max Welling
8. Jimmy Ba
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Abdul Fatir Ansari overall

1. Weili Nie
2. Arash Vahdat

Papers from Abdul Fatir Ansari's perspective

Ordered by how strongly the researchers Abdul Fatir Ansari trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Hyperprior Induced Unsupervised Disentanglement of Latent Representations (2018) — 30 citations
• Chronos: Learning the Language of Time Series (2024) — 878 citations
• Refining Deep Generative Models via Discriminator Gradient Flow (2020) — 53 citations