Kratarth Goel

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 4 papers; trusts 213 researchers and is trusted by 274 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #2,403 of 21,575 by trust here, #2,273 by citations
• Artificial Intelligence — 69 citations into it; #8,788 of 35,345 by trust here, #5,120 by citations
• Model Architectures — 29 citations into it; #1,164 of 2,278 by trust here, #604 by citations
• Automotive Engineering — 24 citations into it; #683 of 3,081 by trust here, #608 by citations
• Signal Processing — 13 citations into it
• Generative Models — 10 citations into it; #659 of 1,956 by trust here, #414 by citations
• Natural Language Processing — 9 citations into it
• Ocean Engineering — 9 citations into it

Whom Kratarth Goel trusts overall

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

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

Who trusts Kratarth Goel overall

1. Nachiket Deo
2. Ye Yuan
3. Kris M. Kitani
4. Nicholas Rhinehart
5. Boris Ivanovic
6. Yu Kong
7. Amir Sadeghian
8. Chen Sun
9. Garrett Wilson
10. Tim Meinhardt

Papers from Kratarth Goel's perspective

Ordered by how strongly the researchers Kratarth Goel 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Recurrent Latent Variable Model for Sequential Data (2015) — 1,375 citations
• Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016) — 3,459 citations
• Wayformer: Motion Forecasting via Simple & Efficient Attention Networks (2022) — 449 citations
• Modeling temporal dependencies in data using a DBN-LSTM (2015) — 34 citations