Nicholas Watters

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 8 papers; trusts 364 researchers and is trusted by 114 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #7,280 of 35,345 by trust here, #3,859 by citations
• Computer Vision and Pattern Recognition — 148 citations into it; #3,124 of 21,575 by trust here, #2,263 by citations
• Representation & Self-Supervised Learning — 79 citations into it; #733 of 3,145 by trust here, #483 by citations
• Reinforcement Learning — 56 citations into it; #2,082 of 2,242 by trust here, #1,004 by citations
• Generative Models — 51 citations into it; #845 of 1,956 by trust here, #661 by citations
• Model Architectures — 24 citations into it
• Optimization & Training — 24 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it

Whom Nicholas Watters trusts overall

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

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

Who trusts Nicholas Watters overall

1. Thomas Kipf
2. Francesco Locatello
3. Aleksandar Stani'c
4. Sjoerd van Steenkiste
5. Christopher P. Burgess
6. Loic Matthey
7. Martin Engelcke
8. J. Urgen Schmidhuber
9. Fei Ye
10. Bernhard Schölkopf

Papers from Nicholas Watters's perspective

Ordered by how strongly the researchers Nicholas Watters 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Multi-Object Representation Learning with Iterative Variational Inference (2019) — 583 citations
• Visual Interaction Networks: Learning a Physics Simulator from Video (2017) — 297 citations
• MONet: Unsupervised Scene Decomposition and Representation (2019) — 617 citations
• Understanding disentangling in β-VAE (2018) — 962 citations
• COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration (2019) — 133 citations
• Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (2018) — 136 citations
• Unsupervised Model Selection for Variational Disentangled Representation Learning (2019) — 89 citations
• Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs (2019) — 188 citations