Shruti Palaskar
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 5 papers; trusts 200 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 76 citations into it; #34,991 of 35,345 by trust here, #12,580 by citations
- Computer Vision and Pattern Recognition — 63 citations into it
- Natural Language Processing — 45 citations into it
- Multimodal Learning — 26 citations into it; #1,186 of 1,211 by trust here, #702 by citations
- Evaluation & Benchmarks — 23 citations into it; #3,101 of 3,147 by trust here, #2,244 by citations
- Signal Processing — 21 citations into it
- Speech & Audio — 18 citations into it
- Model Architectures — 12 citations into it
Whom Shruti Palaskar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Kyunghyun Cho
- Dzmitry Bahdanau
- Oriol Vinyals
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
Who trusts Shruti Palaskar overall
Papers from Shruti Palaskar's perspective
Ordered by how strongly the researchers Shruti Palaskar 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- 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)
Their papers
- Multimodal Abstractive Summarization for How2 Videos (2019) — 110 citations
- How2: A Large-scale Dataset for Multimodal Language Understanding (2018) — 326 citations
- Linguistic Unit Discovery from Multi-Modal Inputs in Unwritten Languages: Summary of the “Speaking Rosetta” JSALT 2017 Workshop (2018) — 34 citations
- How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language (2020) — 353 citations
- Learning from Multiview Correlations in Open-domain Videos (2018) — 11 citations