Varun Prashant Gangal
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 8 papers; trusts 195 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 400 citations into it; #20,968 of 35,345 by trust here, #5,359 by citations
- Natural Language Processing — 352 citations into it
- Computer Vision and Pattern Recognition — 136 citations into it
- Evaluation & Benchmarks — 134 citations into it
- Information Systems — 35 citations into it
- Representation & Self-Supervised Learning — 32 citations into it
- Large Language Models — 29 citations into it
- Optimization & Training — 18 citations into it
Whom Varun Prashant Gangal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Jimmy Ba
- Kaiming He
- Richard Socher
- Oriol Vinyals
Who trusts Varun Prashant Gangal overall
- Sudha Rao
- Steven Y. Feng
- Anna Wegmann
- Harrison Lee
- Zhiqiang Hu
- Parker Riley
- Di Jin
- Alexey Romanov
- Azal Ahmad Khan
Papers from Varun Prashant Gangal's perspective
Ordered by how strongly the researchers Varun Prashant Gangal trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
- A Survey of Data Augmentation Approaches for NLP (2021) — 1,000 citations
- Automatic Construction of Evaluation Suites for Natural Language Generation Datasets (2021) — 27 citations
- Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models (2017) — 190 citations
- A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
- GenAug: Data Augmentation for Finetuning Text Generators (2020) — 78 citations
- NAREOR: The Narrative Reordering Problem (2021) — 27 citations
- (Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas (2019) — 22 citations