Vasile Palade

Artificial Intelligence · 2010–2017

A researcher in the Science corpus: 9 papers; trusts 85 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it; #8,762 of 35,345 by trust here, #8,893 by citations
• Computational Theory and Mathematics — 34 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Information Systems — 24 citations into it
• Electrical and Electronic Engineering — 21 citations into it; #14,005 of 16,539 by trust here, #6,854 by citations
• Molecular Biology — 18 citations into it; #10,632 of 17,773 by trust here, #17,184 by citations
• Evaluation & Benchmarks — 11 citations into it
• Accounting — 10 citations into it

Whom Vasile Palade trusts overall

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

1. Nitesh V. Chawla
2. Gang Wu
3. Gary M. Weiss
4. Vladimir Naumovich Vapni
5. Nathalie Japkowicz
6. Chih-Jen Lin
7. Teuvo Kohonen
8. Jaka Demsar
9. Andreas Holzinger
10. James Kennedy

Who trusts Vasile Palade overall

1. Andreas Holzinger
2. Giulia Vilone
3. Paula Branco
4. Giovanna Menardi
5. Victoria López
6. Alberto Fernández
7. Salvador Garcia
8. Chris Biemann
9. Longo Luca
10. Luís Torgo

Papers from Vasile Palade's perspective

Ordered by how strongly the researchers Vasile Palade 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. The Nature of Statistical Learning Theory
7. Long Short-Term Memory
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

• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013) — 1,580 citations
• Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach (2016) — 97 citations
• Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis (2011) — 34 citations
• Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014) — 30 citations
• Towards Integrative Machine Learning and Knowledge Extraction (2017) — 22 citations
• A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop (2017) — 98 citations
• Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing (2015) — 36 citations
• Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012) — 355 citations
• FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning (2010) — 413 citations