Vineel Pratap
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 9 papers; trusts 138 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 228 citations into it; #8,893 of 35,345 by trust here, #5,565 by citations
- Speech & Audio — 137 citations into it; #300 of 1,076 by trust here, #200 by citations
- Signal Processing — 127 citations into it; #2,602 of 7,572 by trust here, #1,003 by citations
- Natural Language Processing — 107 citations into it; #4,267 of 5,688 by trust here, #2,529 by citations
- Computer Vision and Pattern Recognition — 52 citations into it
- Model Architectures — 45 citations into it
- Evaluation & Benchmarks — 20 citations into it; #1,608 of 3,147 by trust here, #1,505 by citations
- Efficiency & Compression — 18 citations into it
Whom Vineel Pratap trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Graves
- Ronan Collobert
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Kenneth Heafield
- Yann LeCun
Who trusts Vineel Pratap overall
- Alexei Baevski
- Wei-Ning Hsu
- Alexis Conneau
- Qiantong Xu
- Tatiana Likhomanenko
- Jacob Kahn
- Yu Zhang
- Jinyu Li
- Abdel-rahman Mohamed
- Michael Auli
Papers from Vineel Pratap's perspective
Ordered by how strongly the researchers Vineel Pratap 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters (2020) — 172 citations
- End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures (2019) — 264 citations
- MLS: A Large-Scale Multilingual Dataset for Speech Research (2020) — 803 citations
- Rethinking Evaluation in ASR: Are Our Models Robust Enough? (2020) — 111 citations
- Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021) — 279 citations
- Scaling Up Online Speech Recognition Using ConvNets (2020) — 48 citations
- Scaling Speech Technology to 1, 000+ Languages (2023) — 691 citations
- Differentiable Weighted Finite-State Transducers (2020) — 31 citations
- Wav2Letter++: A Fast Open-source Speech Recognition System (2018) — 168 citations