Anmol Gulati

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 9 papers; trusts 77 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 257 citations into it; #13,597 of 35,345 by trust here, #3,384 by citations
• Speech & Audio — 139 citations into it; #507 of 1,076 by trust here, #167 by citations
• Signal Processing — 133 citations into it; #3,214 of 7,572 by trust here, #438 by citations
• Natural Language Processing — 86 citations into it
• Model Architectures — 75 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Optimization & Training — 24 citations into it

Whom Anmol Gulati trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Jimmy Ba
7. Ilya Sutskever
8. Kaiming He
9. Yann LeCun
10. Quoc V. Le

Who trusts Anmol Gulati overall

1. Yu Zhang
2. Jinyu Li
3. Ye Jia
4. Wei Han
5. Chung-Cheng Chiu
6. Ankur Bapna
7. Bo Li
8. Chenpeng Du
9. Yu-An Chung
10. Rohit Prabhavalkar

Papers from Anmol Gulati's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Conformer: Convolution-augmented Transformer for Speech Recognition (2020) — 4,195 citations
• ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context (2020) — 302 citations
• BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021) — 208 citations
• A Better and Faster end-to-end Model for Streaming ASR (2020) — 133 citations
• SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training (2021) — 102 citations
• Scaling End-to-End Models for Large-Scale Multilingual ASR (2021) — 86 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Universal ASR: Unify and Improve Streaming ASR with Full-context Modeling (2020) — 19 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations