Cesare Alippi

Artificial Intelligence · 2008–2023

A researcher in the Science corpus: 8 papers; trusts 275 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 216 citations into it; #23,608 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 78 citations into it
• Signal Processing — 64 citations into it
• Graph Learning — 63 citations into it
• Model Architectures — 40 citations into it
• Building and Construction — 34 citations into it
• Electrical and Electronic Engineering — 32 citations into it; #12,433 of 16,539 by trust here, #7,770 by citations
• Statistical and Nonlinear Physics — 32 citations into it; #8,616 of 10,127 by trust here, #7,402 by citations

Whom Cesare Alippi trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Ronald J. Williams
5. Yann LeCun
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Alex Graves
9. Kaiming He
10. Sepp Hochreiter

Who trusts Cesare Alippi overall

1. Na Kong
2. Winston Khoon Guan Seah
3. Alexander Rainer Tassilo Gepperth

Papers from Cesare Alippi's perspective

Ordered by how strongly the researchers Cesare Alippi 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Learning in Nonstationary Environments: A Survey (2015) — 778 citations
• An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes (2008) — 455 citations
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023) — 498 citations
• Deep Learning for Time Series Forecasting: The Electric Load Case (2019) — 311 citations
• Spectral Clustering with Graph Neural Networks for Graph Pooling (2019) — 551 citations
• Artificial Intelligence in the Age of Neural Networks and Brain Computing (2019)
• Graph Neural Networks With Convolutional ARMA Filters (2019) — 500 citations
• Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018) — 554 citations