Yash Bhalgat

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 93 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 55 citations into it; #15,428 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 53 citations into it; #25,061 of 35,345 by trust here, #20,377 by citations
• Efficiency & Compression — 39 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 10 citations into it

Whom Yash Bhalgat trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Mingxing Tan
7. Quoc V. Le
8. Yann LeCun
9. L. Eon Bottou
10. Ilya Sutskever

Who trusts Yash Bhalgat overall

1. Markus Nagel
2. Yelysei Bondarenko
3. Alexandre D'efossez
4. Marios Fournarakis
5. Itay Hubara
6. Tijmen Blankevoort
7. Zi-Mei Yao
8. Yossi Adi
9. Jianping Gou

Papers from Yash Bhalgat's perspective

Ordered by how strongly the researchers Yash Bhalgat 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LSQ+: Improving low-bit quantization through learnable offsets and better initialization (2020) — 310 citations
• QKD: Quantization-aware Knowledge Distillation (2019) — 82 citations