Kyle Kastner

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 7 papers; trusts 350 researchers and is trusted by 199 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #2,744 of 21,575 by trust here, #2,836 by citations
• Artificial Intelligence — 126 citations into it; #6,843 of 35,345 by trust here, #3,783 by citations
• Model Architectures — 72 citations into it; #2,199 of 2,278 by trust here, #1,378 by citations
• Natural Language Processing — 52 citations into it; #2,519 of 5,688 by trust here, #1,316 by citations
• Optimization & Training — 35 citations into it
• Signal Processing — 31 citations into it
• Speech & Audio — 26 citations into it; #465 of 1,076 by trust here, #250 by citations
• Generative Models — 20 citations into it; #658 of 1,956 by trust here, #414 by citations

Whom Kyle Kastner trusts overall

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

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

Who trusts Kyle Kastner overall

1. Adrian La'ncucki
2. Kainan Peng
3. Yu Zhang
4. Wei Ping
5. Yoshua Bengio
6. Takuma Okamoto
7. Garrett Wilson
8. Kyunghyun Cho
9. Xu Tan
10. Rahul G. Krishnan

Papers from Kyle Kastner's perspective

Ordered by how strongly the researchers Kyle Kastner 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. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• A Recurrent Latent Variable Model for Sequential Data (2015) — 1,375 citations
• ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks (2015) — 282 citations
• ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation (2015) — 267 citations
• Learning Distributed Representations from Reviews for Collaborative Filtering (2015) — 114 citations
• Representation Mixing for TTS Synthesis (2018) — 46 citations
• Char2Wav: End-to-End Speech Synthesis (2017) — 460 citations
• ReSeg: A Recurrent Neural Network for Object Segmentation (2015) — 39 citations