Joan Serrà

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 258 researchers and is trusted by 167 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #2,028 of 35,345 by trust here, #1,972 by citations
• Computer Vision and Pattern Recognition — 80 citations into it; #3,006 of 21,575 by trust here, #2,191 by citations
• Signal Processing — 74 citations into it; #642 of 7,572 by trust here, #575 by citations
• Speech & Audio — 36 citations into it; #206 of 1,076 by trust here, #273 by citations
• Generative Models — 29 citations into it; #411 of 1,956 by trust here, #668 by citations
• Representation & Self-Supervised Learning — 25 citations into it; #380 of 3,145 by trust here, #334 by citations
• Evaluation & Benchmarks — 19 citations into it
• Model Architectures — 15 citations into it

Whom Joan Serrà trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Alex Graves
9. Radford M. Neal
10. Karen Simonyan

Who trusts Joan Serrà overall

1. Zixuan Ke
2. Gyuhak Kim
3. Rahaf Aljundi
4. Bing Liu
5. Tinne Tuytelaars
6. Jaehong Yoon
7. Gobinda Saha
8. Matthias De Lange
9. Matteo Boschini
10. Gido M. van de Ven

Papers from Joan Serrà's perspective

Ordered by how strongly the researchers Joan Serrà 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Overcoming catastrophic forgetting with hard attention to the task (2018) — 1,304 citations
• Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks (2019) — 259 citations
• SEGAN: Speech Enhancement Generative Adversarial Network (2017) — 1,295 citations
• Universal Speech Enhancement with Score-based Diffusion (2022) — 146 citations
• Upsampling Artifacts in Neural Audio Synthesis (2020) — 77 citations
• Input complexity and out-of-distribution detection with likelihood-based generative models (2019) — 315 citations