Sachin S. Talathi

Artificial Intelligence · 2010–2016

A researcher in the Science corpus: 4 papers; trusts 119 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #33,351 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #6,474 of 21,575 by trust here, #7,750 by citations
• Efficiency & Compression — 14 citations into it; #342 of 1,264 by trust here, #613 by citations
• Model Architectures — 10 citations into it; #744 of 2,278 by trust here, #1,092 by citations
• Signal Processing — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Sachin S. Talathi trusts overall

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

1. Alex Krizhevsky
2. James Bergstra
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Kaiming He
6. Karen Simonyan
7. Yann LeCun
8. Ilya Sutskever
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Sachin S. Talathi overall

1. Darryl Dexu Lin
2. Georgios Georgiadis
3. Jeff Johnson
4. Raj Dabre
5. Zhaowei Cai
6. Yury Nahshan
7. Yunhui Guo
8. Ruizhou Ding
9. Itay Hubara
10. Yelysei Bondarenko

Papers from Sachin S. Talathi's perspective

Ordered by how strongly the researchers Sachin S. Talathi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Fixed Point Quantization of Deep Convolutional Networks (2015) — 864 citations
• Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks (2016) — 48 citations
• Hyper-parameter optimization of deep convolutional networks for object recognition (2015) — 25 citations
• Granger Causality Relationships between Local Field Potentials in an Animal Model of Temporal Lobe Epilepsy (2010) — 50 citations