Maurizio Zazzi
Artificial Intelligence · 2008–2017
A researcher in the Science corpus: 8 papers; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it
- Infectious Diseases — 15 citations into it; #459 of 589 by trust here, #328 by citations
- Virology — 13 citations into it; #66 of 84 by trust here, #30 by citations
- Statistics and Probability — 9 citations into it
- Evaluation & Benchmarks — 6 citations into it
Whom Maurizio Zazzi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mattia C. F. Prosperi
- André Altmann
- Michal Rosen-Zvi
- Michael I. Jordan
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Sepp Hochreiter
- Tom Fawcett
- Alex Krizhevsky
Who trusts Maurizio Zazzi overall
Papers from Maurizio Zazzi's perspective
Ordered by how strongly the researchers Maurizio Zazzi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Distilling the Knowledge in a Neural Network (arXiv.org)
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Their papers
- Combining Kernel and Model Based Learning for HIV Therapy Selection (2017) — 84 citations
- Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017) — 300 citations
- History-alignment models for bias-aware prediction of virological response to HIV combination therapy (2012) — 10 citations
- Predicting Response to Antiretroviral Treatment by Machine Learning: The EuResist Project (2012) — 59 citations
- Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study) (2010) — 54 citations
- Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models (2010) — 110 citations
- Selecting anti-HIV therapies based on a variety of genomic and clinical factors (2008) — 75 citations
- Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment (2008) — 55 citations