Apoorv Vyas
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 8 papers; trusts 227 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 259 citations into it; #9,687 of 35,345 by trust here, #4,559 by citations
- Signal Processing — 157 citations into it
- Speech & Audio — 145 citations into it
- Computer Vision and Pattern Recognition — 112 citations into it; #9,260 of 21,575 by trust here, #4,212 by citations
- Generative Models — 61 citations into it
- Natural Language Processing — 59 citations into it
- Model Architectures — 56 citations into it; #1,035 of 2,278 by trust here, #612 by citations
- Evaluation & Benchmarks — 32 citations into it
Whom Apoorv Vyas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Ilya Sutskever
- Yann LeCun
- Kaiming He
- Jimmy Ba
- J. Urgen Schmidhuber
Who trusts Apoorv Vyas overall
- Tri Dao
- Sachin Mehta
- Yunyang Xiong
- Yi Tay
- Qing Yu
- Shijie Geng
- Irwan Bello
- Shuhao Cao
- Peng Gao
- Vikash Sehwag
Papers from Apoorv Vyas's perspective
Ordered by how strongly the researchers Apoorv Vyas 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- 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)
Their papers
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (2020) — 2,953 citations
- Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers (2018) — 267 citations
- Fast Transformers with Clustered Attention (2020) — 182 citations
- Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale (2023) — 551 citations
- Audiobox: Unified Audio Generation with Natural Language Prompts (2023) — 175 citations
- Movie Gen: A Cast of Media Foundation Models (2024) — 592 citations
- Scaling Speech Technology to 1, 000+ Languages (2023) — 691 citations
- Generative Pre-training for Speech with Flow Matching (2023) — 72 citations