Francisco Pereira

Artificial Intelligence · with Bandettini · 2018

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Optimization & Training — 5 citations into it

Whom Francisco Pereira trusts overall

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

1. Diederik P. Kingma

Papers from Francisco Pereira's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
3. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
4. A Stochastic Approximation Method
5. Multi-Scale Context Aggregation by Dilated Convolutions
6. Weight Uncertainty in Neural Networks
7. Practical Variational Inference for Neural Networks
8. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
9. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
10. Overcoming catastrophic forgetting in neural networks

Their papers

• Distributed Weight Consolidation: A Brain Segmentation Case Study (2018) — 28 citations