Vidhisha Balachandran

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 6 papers; trusts 436 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 360 citations into it; #10,579 of 35,345 by trust here, #9,760 by citations
• Natural Language Processing — 290 citations into it; #2,054 of 5,688 by trust here, #2,031 by citations
• Safety, Robustness & Interpretability — 119 citations into it
• Computer Vision and Pattern Recognition — 88 citations into it
• Evaluation & Benchmarks — 64 citations into it; #1,163 of 3,147 by trust here, #1,876 by citations
• Large Language Models — 58 citations into it
• Information Retrieval & Search — 37 citations into it
• Information Systems — 30 citations into it

Whom Vidhisha Balachandran trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Richard Socher
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Jacob Devlin

Who trusts Vidhisha Balachandran overall

1. Sachin Kumar
2. Esin Durmus
3. Faisal Ladhak
4. Yulia Tsvetkov
5. David Wan
6. Mingqi Gao
7. Leonardo F. R. Ribeiro
8. Mengyao Cao
9. Philippe Laban
10. Ashwin Devaraj

Papers from Vidhisha Balachandran's perspective

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

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

Their papers

• Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021) — 396 citations
• Differentiable Reasoning over a Virtual Knowledge Base (2020) — 91 citations
• SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (2021) — 80 citations
• Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022) — 61 citations
• Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022) — 119 citations
• Simple and Efficient ways to Improve REALM (2021) — 5 citations