Michael Chinen
Signal Processing · 2018–2023
A researcher in the Science corpus: 6 papers; trusts 15 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 76 citations into it; #5,571 of 7,572 by trust here, #2,457 by citations
- Speech & Audio — 54 citations into it; #627 of 1,076 by trust here, #642 by citations
- Artificial Intelligence — 45 citations into it; #26,414 of 35,345 by trust here, #16,166 by citations
- Computer Vision and Pattern Recognition — 39 citations into it
- Generative Models — 19 citations into it
- Cognitive Neuroscience — 9 citations into it
- Evaluation & Benchmarks — 9 citations into it; #2,376 of 3,147 by trust here, #2,743 by citations
- Computational Mechanics — 8 citations into it
Whom Michael Chinen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrew Hines
- Chih-Jen Lin
- Jan Skoglund
- Naomi Harte
- Jean-Marc Valin
- Aäron van den Oord
- Tong Mo
- Hannes Gamper
- Diederik P. Kingma
- Geoffrey E. Hinton
Who trusts Michael Chinen overall
- Hubert Siuzdak
- Neil Zeghidour
- Alexandre D'efossez
- Ahmed Omran
- Hakan Erdogan
- Rithesh Kumar
- Alejandro Luebs
- Zalán Borsos
- Xue Jiang
- Teerapat Jenrungrot
Papers from Michael Chinen's perspective
Ordered by how strongly the researchers Michael Chinen trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- GENERATIVE ADVERSARIAL NETS
- 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)
- Going deeper with convolutions
Their papers
- ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric (2020) — 229 citations
- Robust Low Rate Speech Coding Based on Cloned Networks and Wavenet (2020) — 13 citations
- LMCodec: A Low Bitrate Speech Codec with Causal Transformer Models (2023) — 31 citations
- Generative Speech Coding with Predictive Variance Regularization (2021) — 79 citations
- AMBIQUAL - a full reference objective quality metric for ambisonic spatial audio (2018) — 24 citations
- Differentiable Consistency Constraints for Improved Deep Speech Enhancement (2018) — 131 citations