Siddharth Arora

Artificial Intelligence · with Khapra · 2013–2021

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #23,936 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 72 citations into it; #4,659 of 5,688 by trust here, #3,574 by citations
• Electrical and Electronic Engineering — 22 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Management Science and Operations Research — 9 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it
• Information Systems — 5 citations into it

Whom Siddharth Arora trusts overall

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

1. James W. Taylor
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Jimmy Ba
6. Alex Krizhevsky
7. Ilya Sutskever
8. Richard Socher
9. Yann LeCun
10. Matthew D. Zeiler

Who trusts Siddharth Arora overall

1. Wen Zheng
2. Haolan Zhan
3. Byeongchang Kim
4. Bill Byrne
5. Zhibin Liu
6. Lin Wang

Papers from Siddharth Arora's perspective

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

Their papers

• Towards Exploiting Background Knowledge for Building Conversation Systems (2018) — 174 citations
• Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining (2020) — 83 citations
• Review of low voltage load forecasting: Methods, applications, and recommendations (2021) — 155 citations
• On Knowledge distillation from complex networks for response prediction (2019) — 16 citations
• Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods (2013) — 104 citations