Asit K. Mishra

Computer Vision and Pattern Recognition · 2010–2021

A researcher in the Science corpus: 7 papers; trusts 327 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #4,829 of 21,575 by trust here, #4,926 by citations
• Artificial Intelligence — 100 citations into it; #6,302 of 35,345 by trust here, #7,112 by citations
• Efficiency & Compression — 59 citations into it
• Model Architectures — 31 citations into it
• Hardware and Architecture — 28 citations into it; #1,373 of 1,551 by trust here, #281 by citations
• Computer Networks and Communications — 24 citations into it; #8,785 of 9,886 by trust here, #2,358 by citations
• Optimization & Training — 19 citations into it
• ML Systems & Hardware — 11 citations into it

Whom Asit K. Mishra trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Kaiming He
5. Matthieu Courbariaux
6. Christian Szegedy
7. Yann LeCun
8. Ilya Sutskever
9. Diederik P. Kingma
10. X. Zhang

Who trusts Asit K. Mishra overall

1. Qing Jin
2. Sungho Shin
3. Lin Wang
4. Yoonho Boo
5. Chaim Baskin
6. Raghuraman Krishnamoorthi
7. Aojun Zhou
8. Jianping Gou
9. Jungwook Choi
10. Tejalal Choudhary

Papers from Asit K. Mishra's perspective

Ordered by how strongly the researchers Asit K. Mishra 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy (2017) — 354 citations
• WRPN: Wide Reduced-Precision Networks (2017) — 278 citations
• From high-level deep neural models to FPGAs (2016) — 500 citations
• Accelerating Sparse Deep Neural Networks (2021) — 344 citations
• Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC (2016) — 318 citations
• OWL: cooperative thread array aware scheduling techniques for improving GPGPU performance (2013) — 301 citations
• Towards characterizing cloud backend workloads: insights from Google compute clusters (2010) — 435 citations