Diganta Misra

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 1,025 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #4,036 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #3,738 of 21,575 by trust here, #9,435 by citations
• Model Architectures — 48 citations into it
• Optimization & Training — 35 citations into it
• Efficiency & Compression — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 7 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Diganta Misra trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. L. Eon Bottou
5. Christian Szegedy
6. Ross B. Girshick
7. Ilya Sutskever
8. Yoshua Bengio
9. Sergey Ioffe
10. Diederik P. Kingma

Who trusts Diganta Misra overall

1. Juan R. Terven
2. Diana-Margarita Córdova-Esparza
3. Alexey Bochkovskiy
4. Shiv Ram Dubey
5. David Stutz
6. Nicklas Hansen
7. Michael Janner
8. Asifullah Khan
9. Yilun Du
10. Anurag Ajay

Papers from Diganta Misra's perspective

Ordered by how strongly the researchers Diganta Misra 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Mish: A Self Regularized Non-Monotonic Neural Activation Function (2019) — 753 citations
• Mish: A Self Regularized Non-Monotonic Activation Function (2020) — 1,134 citations
• Scaling the Number of Tasks in Continual Learning (2022) — 10 citations
• Rotate to Attend: Convolutional Triplet Attention Module (2020) — 989 citations