Anuroop Sriram

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 14 papers; trusts 108 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 217 citations into it; #5,737 of 35,345 by trust here, #3,957 by citations
• Signal Processing — 103 citations into it; #2,800 of 7,572 by trust here, #770 by citations
• Speech & Audio — 103 citations into it; #372 of 1,076 by trust here, #196 by citations
• Natural Language Processing — 97 citations into it; #1,878 of 5,688 by trust here, #446 by citations
• Computer Vision and Pattern Recognition — 93 citations into it
• Model Architectures — 69 citations into it
• Optimization & Training — 33 citations into it
• Evaluation & Benchmarks — 26 citations into it; #1,778 of 3,147 by trust here, #1,728 by citations

Whom Anuroop Sriram trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Graves
5. Sepp Hochreiter
6. Ilya Sutskever
7. Alex Krizhevsky
8. Oriol Vinyals
9. Jimmy Ba
10. Dzmitry Bahdanau

Who trusts Anuroop Sriram overall

1. Jinyu Li
2. Tara N. Sainath
3. Bo Li
4. Peidong Wang
5. Rohit Prabhavalkar
6. Hyungjin Chung
7. Felix Stahlberg
8. Angela Fan
9. Linhao Dong
10. Alexei Baevski

Papers from Anuroop Sriram's perspective

Ordered by how strongly the researchers Anuroop Sriram trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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
• Cold Fusion: Training Seq2Seq Models Together with Language Models (2017) — 311 citations
• Exploring neural transducers for end-to-end speech recognition (2017) — 234 citations
• MLS: A Large-Scale Multilingual Dataset for Speech Research (2020) — 803 citations
• Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021) — 279 citations
• The Open Catalyst 2020 (OC20) Dataset and Community Challenges (2020) — 822 citations
• An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (2020) — 83 citations
• fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. (2020) — 514 citations
• The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts (2022) — 346 citations
• Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021) — 295 citations
• End-to-End Variational Networks for Accelerated MRI Reconstruction (2020) — 436 citations
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018) — 1,072 citations
• Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015) — 3,177 citations