Leonardo Badino

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 4 papers; trusts 109 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it; #14,202 of 35,345 by trust here, #16,471 by citations
• Signal Processing — 27 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #1,556 of 3,145 by trust here, #2,042 by citations
• Speech & Audio — 14 citations into it
• Model Architectures — 12 citations into it
• Natural Language Processing — 11 citations into it; #3,722 of 5,688 by trust here, #4,032 by citations
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Leonardo Badino trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Andrew L. Maas
5. Ruslan Salakhutdinov
6. Diederik P. Kingma
7. László Tóth
8. Vinod Nair
9. Yann LeCun
10. Radford M. Neal

Who trusts Leonardo Badino overall

1. Herman Kamper
2. Maarten Versteegh
3. Aren Jansen
4. Ryan Eloff
5. Taira Tsuchiya
6. Lasse Borgholt
7. Thomas Schatz
8. Benjamin van Niekerk
9. Emmanuel Dupoux
10. Sharon Goldwater

Papers from Leonardo Badino's perspective

Ordered by how strongly the researchers Leonardo Badino trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• An auto-encoder based approach to unsupervised learning of subword units (2014) — 83 citations
• Discovering discrete subword units with binarized autoencoders and hidden-Markov-model encoders (2015) — 46 citations
• Conditional-Computation-Based Recurrent Neural Networks for Computationally Efficient Acoustic Modelling (2018)
• Phonetic Context Embeddings for DNN-HMM Phone Recognition (2016) — 8 citations