Adrian La'ncucki

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 110 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #3,613 of 35,345 by trust here, #11,921 by citations
• Signal Processing — 33 citations into it; #456 of 7,572 by trust here, #1,958 by citations
• Speech & Audio — 29 citations into it; #131 of 1,076 by trust here, #539 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Natural Language Processing — 19 citations into it
• Generative Models — 18 citations into it
• Model Architectures — 6 citations into it; #298 of 2,278 by trust here, #1,402 by citations

Whom Adrian La'ncucki trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Ryan J. Prenger
6. Jacob Devlin
7. Ashish Vaswani
8. Jaehyeon Kim
9. Rafael Valle
10. Alex Krizhevsky

Who trusts Adrian La'ncucki overall

1. D. Lim
2. Stanislav Beliaev
3. Chung-Ming Chien
4. Jaesung Tae
5. Xu Tan
6. Sofoklis Kakouros
7. Won Jang
8. Hubert Siuzdak
9. Min-Jae Hwang
10. Rohan Badlani

Papers from Adrian La'ncucki's perspective

Ordered by how strongly the researchers Adrian La'ncucki 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning (2020) — 29 citations
• Fastpitch: Parallel Text-to-Speech with Pitch Prediction (2020) — 415 citations
• One TTS Alignment to Rule Them All (2021) — 106 citations