Dominik Roblek
Signal Processing · 2018–2022
A researcher in the Science corpus: 6 papers; trusts 163 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 91 citations into it; #2,093 of 7,572 by trust here, #770 by citations
- Artificial Intelligence — 88 citations into it; #14,567 of 35,345 by trust here, #8,210 by citations
- Computer Vision and Pattern Recognition — 73 citations into it; #19,133 of 21,575 by trust here, #15,955 by citations
- Speech & Audio — 47 citations into it; #489 of 1,076 by trust here, #347 by citations
- Evaluation & Benchmarks — 19 citations into it
- Cognitive Neuroscience — 14 citations into it
- Model Architectures — 14 citations into it
- Optimization & Training — 10 citations into it
Whom Dominik Roblek trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Karen Simonyan
- Diederik P. Kingma
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Andrew L. Maas
- Andrew Zisserman
- Ilya Sutskever
- Yann LeCun
Who trusts Dominik Roblek overall
Papers from Dominik Roblek's perspective
Ordered by how strongly the researchers Dominik Roblek trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- AudioLM: A Language Modeling Approach to Audio Generation (2022) — 990 citations
- Fréchet Audio Distance: A Reference-Free Metric for Evaluating Music Enhancement Algorithms (2019) — 342 citations
- Real-Time Speech Frequency Bandwidth Extension (2020) — 71 citations
- SEANet: A Multi-modal Speech Enhancement Network (2020) — 117 citations
- Self-supervised audio representation learning for mobile devices (2019) — 48 citations
- Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms (2018) — 357 citations