M. I. M. Fernando

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 27 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Electrical and Electronic Engineering — 9 citations into it
• Optimization & Training — 8 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom M. I. M. Fernando trusts overall

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

1. L. Eon Bottou
2. Leo Breiman
3. Diederik P. Kingma
4. Yoav Freund
5. Nitesh V. Chawla
6. Xu-Ying Liu
7. Mingxing Tan
8. Geoffrey E. Hinton
9. Tomasz Maciejewski
10. Gary King

Papers from M. I. M. Fernando's perspective

Ordered by how strongly the researchers M. I. M. Fernando 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks (2021) — 299 citations