Karishma Malkan
Artificial Intelligence · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 94 citations into it; #19,640 of 35,345 by trust here, #10,207 by citations
- Natural Language Processing — 56 citations into it; #3,361 of 5,688 by trust here, #3,190 by citations
- Computer Vision and Pattern Recognition — 52 citations into it
- Model Architectures — 33 citations into it
- Optimization & Training — 17 citations into it
- Safety, Robustness & Interpretability — 15 citations into it
- Evaluation & Benchmarks — 12 citations into it
Whom Karishma Malkan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Graves
- Geoffrey E. Hinton
- Noam Shazeer
- Ilya Sutskever
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Jimmy Ba
- Kaiming He
- Yann LeCun
Who trusts Karishma Malkan overall
Papers from Karishma Malkan's perspective
Ordered by how strongly the researchers Karishma Malkan 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
Their papers
- Do Transformer Modifications Transfer Across Implementations and Applications? (2021) — 138 citations
- WT5?! Training Text-to-Text Models to Explain their Predictions (2020) — 219 citations