Adam R. Kosiorek
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 8 papers; trusts 608 researchers and is trusted by 467 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 161 citations into it; #1,960 of 21,575 by trust here, #3,633 by citations
- Artificial Intelligence — 141 citations into it; #16,379 of 35,345 by trust here, #10,065 by citations
- Generative Models — 47 citations into it; #585 of 1,956 by trust here, #759 by citations
- Model Architectures — 45 citations into it; #1,132 of 2,278 by trust here, #1,104 by citations
- Probabilistic & Bayesian Methods — 38 citations into it; #1,485 of 1,525 by trust here, #921 by citations
- Representation & Self-Supervised Learning — 35 citations into it; #2,304 of 3,145 by trust here, #2,178 by citations
- Optimization & Training — 17 citations into it
- Reinforcement Learning — 11 citations into it
Whom Adam R. Kosiorek trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Jimmy Ba
- Ilya Sutskever
- J. Urgen Schmidhuber
- Ruslan Salakhutdinov
Who trusts Adam R. Kosiorek overall
- Klaus Greff
- Jindong Jiang
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
- Sjoerd van Steenkiste
- Yoshua Bengio
- Francesco Locatello
- Ruslan Salakhutdinov
- Nitish Srivastava
- Daniel Zoran
Papers from Adam R. Kosiorek's perspective
Ordered by how strongly the researchers Adam R. Kosiorek trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- 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)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects (2018) — 277 citations
- Set Transformer (2018) — 267 citations
- GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations (2019) — 343 citations
- Stacked Capsule Autoencoders (2019) — 280 citations
- Hierarchical Attentive Recurrent Tracking (2017) — 63 citations
- Conditional Set Generation with Transformers (2020) — 44 citations
- Tighter Variational Bounds are Not Necessarily Better (2018) — 217 citations
- End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019) — 16 citations