Aparajita Haldar

Natural Language Processing · 2018

A researcher in the Science corpus: 3 papers; trusts 65 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 113 citations into it; #1,290 of 5,688 by trust here, #1,214 by citations
• Artificial Intelligence — 101 citations into it; #5,645 of 35,345 by trust here, #5,002 by citations
• Evaluation & Benchmarks — 37 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Aparajita Haldar trusts overall

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

1. Rachel Rudinger
2. Benjamin Van Durme
3. Johan Bos
4. Samuel R. Bowman
5. Geoffrey E. Hinton
6. Maite Taboada
7. Christopher D. Manning
8. Adam Poliak
9. Yoshua Bengio
10. Bill MacCartney

Who trusts Aparajita Haldar overall

1. Yonatan Belinkov
2. Kyle Richardson
3. Ellie Pavlick
4. James Thorne
5. Adam Poliak
6. Joe Stacey
7. Eric Wallace
8. Yejin Choi
9. Tal Schuster
10. Divyansh Kaushik

Papers from Aparajita Haldar's perspective

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

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

Their papers

• Hypothesis Only Baselines in Natural Language Inference (2018) — 638 citations
• Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018) — 10 citations
• Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations (2018) — 12 citations