Siddhant Garg

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #9,530 of 35,345 by trust here, #15,236 by citations
• Natural Language Processing — 27 citations into it; #2,396 of 5,688 by trust here, #4,250 by citations
• Safety, Robustness & Interpretability — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #8,331 of 21,575 by trust here, #14,822 by citations
• Signal Processing — 8 citations into it
• Large Language Models — 6 citations into it

Whom Siddhant Garg trusts overall

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

1. Diederik P. Kingma
2. Bo Pang
3. Geoffrey E. Hinton
4. Jacob Devlin
5. Sepp Hochreiter
6. Yoshua Bengio
7. Yoon Kim
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Siddhant Garg overall

1. Alessandro Moschitti
2. Luca Soldaini
3. Thuy Vu
4. Chuan Guo
5. Dong Wang
6. Jimmy J. Lin
7. Andreas Ruckl'e
8. Rujun Han
9. John X. Morris
10. Chao-Chun Hsu

Papers from Siddhant Garg's perspective

Ordered by how strongly the researchers Siddhant Garg 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• BAE: BERT-based Adversarial Examples for Text Classification (2020) — 669 citations
• TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection (2019) — 233 citations