Vamsi Krishna Aribandi
Artificial Intelligence · 2021–2022
A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 230 citations into it; #35,057 of 35,345 by trust here, #12,761 by citations
- Natural Language Processing — 162 citations into it
- Computer Vision and Pattern Recognition — 93 citations into it
- Evaluation & Benchmarks — 73 citations into it
- Large Language Models — 50 citations into it
- Model Architectures — 39 citations into it
- Representation & Self-Supervised Learning — 29 citations into it
- Safety, Robustness & Interpretability — 17 citations into it
Whom Vamsi Krishna Aribandi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Noam Shazeer
- Yoshua Bengio
- Alex Krizhevsky
- Diederik P. Kingma
- Ilya Sutskever
- Richard Socher
- Kaiming He
- Yann LeCun
- J. Urgen Schmidhuber
Who trusts Vamsi Krishna Aribandi overall
Papers from Vamsi Krishna Aribandi's perspective
Ordered by how strongly the researchers Vamsi Krishna Aribandi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021) — 240 citations
- Are Pretrained Convolutions Better than Pretrained Transformers? (2021) — 54 citations
- OmniNet: Omnidirectional Representations from Transformers (2021) — 33 citations
- HyperPrompt: Prompt-based Task-Conditioning of Transformers (2022) — 114 citations
- How Reliable are Model Diagnostics? (2021) — 21 citations