Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

9 August 2020

Papers citing "Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges"

8 / 8 papers shown

Title
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness Javier Poyatos Daniel Molina Aitor Martínez Javier Del Ser Francisco Herrera 30 10 0 20 Feb 2023
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization Alejandro Rosales-Pérez S. García Francisco Herrera 11 15 0 21 Apr 2022
A Survey on Evolutionary Neural Architecture Search Yuqiao Liu Y. Sun Bing Xue Mengjie Zhang Gary G. Yen Kay Chen Tan 112 403 0 25 Aug 2020
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 183 1,027 0 06 Mar 2020
Real-time Federated Evolutionary Neural Architecture Search Hangyu Zhu Yaochu Jin FedML 131 71 0 04 Mar 2020
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal S. Tabik R. F. Alvear-Sandoval María M. Ruiz J. Sancho-Gómez A. Figueiras-Vidal Francisco Herrera 54 33 0 30 Jan 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution T. Elsken J. H. Metzen Frank Hutter 117 498 0 24 Apr 2018