Ioannis Ch. Paschalidis

Artificial Intelligence · 2000–2021

A researcher in the Science corpus: 9 papers; trusts 62 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Computer Networks and Communications — 51 citations into it
• Optimization & Training — 39 citations into it
• Statistics and Probability — 26 citations into it
• Computational Mechanics — 22 citations into it
• Management Science and Operations Research — 22 citations into it
• Reinforcement Learning — 22 citations into it
• Control and Systems Engineering — 10 citations into it

Whom Ioannis Ch. Paschalidis trusts overall

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

1. Angelia Nedić
2. Robert Tibshirani
3. Peter J. Rousseeuw
4. John Schulman
5. Trevor J. Hastie
6. Philippe Huber
7. Dimitri P. Bertsekas
8. Richard S. Sutton
9. Frank P. Kelly
10. Geoffrey E. Hinton

Who trusts Ioannis Ch. Paschalidis overall

1. Anastasia Koloskova
2. Mung Chiang
3. Theodora S. Brisimi

Papers from Ioannis Ch. Paschalidis's perspective

Ordered by how strongly the researchers Ioannis Ch. Paschalidis 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generalized Proximal Policy Optimization with Sample Reuse (2021) — 73 citations
• Predicting diabetes-related hospitalizations based on electronic health records (2018) — 35 citations
• Federated learning of predictive models from federated Electronic Health Records (2018) — 938 citations
• A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent (2019) — 18 citations
• A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent (2019) — 78 citations
• Outlier detection using distributionally robust optimization under the Wasserstein metric (2017)
• A joint sparse clustering and classification approach with applications to hospitalization prediction (2016) — 9 citations
• Prediction of hospitalization due to heart diseases by supervised learning methods (2014) — 140 citations
• Congestion-dependent pricing of network services (2000) — 405 citations