Jishnu Mukhoti

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 689 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #24,709 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 113 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Model Architectures — 25 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Natural Language Processing — 17 citations into it

Whom Jishnu Mukhoti trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Radford M. Neal
8. Max Welling
9. Ilya Sutskever
10. Yarin Gal

Who trusts Jishnu Mukhoti overall

1. Fredrik K. Gustafsson
2. Wesley J. Maddox
3. Moloud Abdar
4. Rebecca Roelofs
5. Zhipeng Ding
6. Martin Danelljan
7. Hongxin Wei
8. Timur Garipov
9. Thomas Bo Schön
10. K. Ruwani

Papers from Jishnu Mukhoti's perspective

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

Their papers

• Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning (2022) — 144 citations
• Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty (2021) — 99 citations
• Calibrating Deep Neural Networks using Focal Loss (2020) — 646 citations
• Evaluating Bayesian Deep Learning Methods for Semantic Segmentation (2018) — 268 citations