Jugal Kumar Kalita

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 517 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 221 citations into it; #13,741 of 35,345 by trust here, #10,319 by citations
• Natural Language Processing — 153 citations into it
• Computer Vision and Pattern Recognition — 82 citations into it
• Computer Networks and Communications — 37 citations into it; #3,057 of 9,886 by trust here, #3,155 by citations
• Model Architectures — 36 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Signal Processing — 24 citations into it

Whom Jugal Kumar Kalita trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yann LeCun
7. J. Urgen Schmidhuber
8. Richard Socher
9. Alec Radford
10. Alex Graves

Who trusts Jugal Kumar Kalita overall

1. Paulo Angelo Alves Resende
2. Anna L. Buczak
3. B. Ingre

Papers from Jugal Kumar Kalita's perspective

Ordered by how strongly the researchers Jugal Kumar Kalita 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multi-task learning for natural language processing in the 2020s: where are we going? (2020) — 93 citations
• A Survey of the Usages of Deep Learning for Natural Language Processing (2020) — 1,660 citations
• MIFS-ND: A mutual information-based feature selection method (2014) — 421 citations
• Network Anomaly Detection: Methods, Systems and Tools (2014) — 1,294 citations