Manish Kumar

Artificial Intelligence · with Duh · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #13,796 of 35,345 by trust here, #7,657 by citations
• Natural Language Processing — 81 citations into it; #4,859 of 5,688 by trust here, #4,601 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #12,454 of 21,575 by trust here, #13,542 by citations
• Optimization & Training — 23 citations into it
• Large Language Models — 10 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Manish Kumar trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Philipp Koehn
4. Alex Graves
5. Markus Freitag
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Jimmy Ba
9. Alex Krizhevsky
10. Ronan Collobert

Who trusts Manish Kumar overall

1. Xin Wang
2. Dana Ruiter
3. Yudong Chen
4. Josef van Genabith
5. Petru Soviany
6. Philipp Koehn
7. Eduardo Mosqueira-Rey
8. Wenwu Zhu
9. Xuan Zhang
10. Cristina España-Bonet

Papers from Manish Kumar's perspective

Ordered by how strongly the researchers Manish Kumar trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Attention is All you Need (Neural Information Processing Systems)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• DyNet: The Dynamic Neural Network Toolkit (2017) — 391 citations
• Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (2019) — 79 citations
• Curriculum Learning for Domain Adaptation in Neural Machine Translation (2019) — 131 citations
• Editorial Board Editor-in-chief Editorial Staff Contents Articles Joshua 6: a Phrase-based and Hierarchical Statistical Machine Translation System Sampling Phrase Tables for the Moses Statistical Machine Translation System Ulrich Germann Mt-compareval: Graphical Evaluation Interface for Machine Tran — 15 citations
• An Empirical Exploration of Curriculum Learning for Neural Machine Translation (2018) — 125 citations