Trevor Strohman

Artificial Intelligence · with Wang · 2018–2025

A researcher in the Science corpus: 10 papers; trusts 76 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 210 citations into it; #15,444 of 35,345 by trust here, #9,641 by citations
• Speech & Audio — 133 citations into it; #666 of 1,076 by trust here, #570 by citations
• Signal Processing — 119 citations into it; #3,360 of 7,572 by trust here, #1,351 by citations
• Natural Language Processing — 81 citations into it
• Model Architectures — 49 citations into it; #2,101 of 2,278 by trust here, #2,243 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Optimization & Training — 12 citations into it

Whom Trevor Strohman 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. Ilya Sutskever
6. Sepp Hochreiter
7. Mike Schuster
8. Tara N. Sainath
9. J. Urgen Schmidhuber
10. Jimmy Ba

Who trusts Trevor Strohman overall

1. Jinyu Li
2. Tara N. Sainath

Papers from Trevor Strohman's perspective

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

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

Their papers

• Toward Domain-Invariant Speech Recognition via Large Scale Training (2018) — 114 citations
• A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency (2020) — 228 citations
• A Better and Faster end-to-end Model for Streaming ASR (2020) — 133 citations
• Recognizing Long-Form Speech Using Streaming End-to-End Models (2019) — 137 citations
• Two-Pass End-to-End Speech Recognition (2019) — 163 citations
• Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023) — 396 citations
• An Attention-Based Joint Acoustic and Text on-Device End-To-End Model (2020) — 16 citations
• Context-Aware end-to-end ASR Using Self-Attentive Embedding and Tensor Fusion (2023) — 16 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging (2020) — 37 citations