Julian Weber

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #26,405 of 35,345 by trust here, #15,542 by citations
• Speech & Audio — 43 citations into it; #806 of 1,076 by trust here, #600 by citations
• Signal Processing — 41 citations into it
• Generative Models — 11 citations into it

Whom Julian Weber trusts overall

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

1. Jaehyeon Kim
2. Joon Son Chung
3. Jungil Kong
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Ilya Loshchilov
7. Yoshua Bengio
8. Alex Krizhevsky
9. Aäron van den Oord
10. Andrew Zisserman

Who trusts Julian Weber overall

1. Cong Han
2. Nima Mesgarani
3. Ziyue Jiang
4. Zhijun Liu
5. Minki Kang
6. Matt Le

Papers from Julian Weber's perspective

Ordered by how strongly the researchers Julian Weber 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone (2021) — 643 citations
• XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model (2024) — 303 citations