Soumya Batra

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 7 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 204 citations into it
• Natural Language Processing — 145 citations into it
• Information Systems — 17 citations into it

Whom Soumya Batra trusts overall

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

1. Ondrej Dusek
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Dzmitry Bahdanau
7. Claire Gardent
8. Alex Krizhevsky
9. Michael Lewis
10. Jimmy Ba

Who trusts Soumya Batra overall

1. Peyman Heidari
2. Shreyan Bakshi
3. Shashank Jain

Papers from Soumya Batra's perspective

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

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

Their papers

• Llama 2: Open Foundation and Fine-Tuned Chat Models (2023) — 17,670 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Building Adaptive Acceptability Classifiers for Neural NLG (2021) — 16 citations
• Getting to Production with Few-shot Natural Language Generation Models (2021) — 17 citations
• Structure-to-Text Generation with Self-Training, Acceptability Classifiers and Context-Conditioning for the GEM Shared Task (2021)
• Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020) — 10 citations