Santiago Pascual

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 6 papers; trusts 212 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #4,311 of 35,345 by trust here, #5,221 by citations
• Signal Processing — 87 citations into it; #547 of 7,572 by trust here, #452 by citations
• Computer Vision and Pattern Recognition — 61 citations into it
• Speech & Audio — 57 citations into it; #170 of 1,076 by trust here, #210 by citations
• Generative Models — 30 citations into it; #387 of 1,956 by trust here, #644 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,722 of 3,145 by trust here, #1,395 by citations
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Santiago Pascual trusts overall

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

1. Yoshua Bengio
2. Mirco Ravanelli
3. Geoffrey E. Hinton
4. Sepp Hochreiter
5. Diederik P. Kingma
6. Kaiming He
7. Alex Krizhevsky
8. Karen Simonyan
9. Christian Szegedy
10. Yann LeCun

Who trusts Santiago Pascual overall

1. Yu-An Chung
2. Ashutosh Pandey
3. Chris Donahue
4. Igor Jauk
5. Mira Lilleholt Vik
6. James R. Glass
7. Yuichiro Koyama
8. Hyeong-Seok Choi
9. Deliang Wang
10. Yi Luo

Papers from Santiago Pascual's perspective

Ordered by how strongly the researchers Santiago Pascual 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks (2019) — 259 citations
• Multi-Task Self-Supervised Learning for Robust Speech Recognition (2020) — 310 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
• Multi-output RNN-LSTM for multiple speaker speech synthesis with α-interpolation model (2016) — 12 citations