- GA-based reinforcement learning for neural networks
- Genetic Reinforcement Learning Neurocontrol Problems
- Genetic Algorithms and Neural Networks
- Genetic algorithms and neural networks: optimizing connections and connectivity
- Combining Genetic Algorithms and Neural Networks: The Encoding Problem – PhD Thesis – Philipp Koehn
- Genetic cascade learning for neural networks
- Evolving Optimal Neural Networks Using Genetic Algorithms with Occam’s Razor
- Exploring Strategies for Training Deep Neural Networks
- BAYESIAN LEARNING FOR NEURAL NETWORKS- PhD Thesis – Radford M. Neal
- Genetic Algorithms and Machine Learning
- Machine Learning: Introduction to Genetic Algorithms
- Neural Networks and Evolutionary Computation. Part I: Hybrid Approaches in Artificial Intelligence
- Neural Networks and Evolutionary Computation. Part II: Hybrid Approaches in the Neurosciences
- Artifi cial Neural Networks Design using Evolutionary Algorithms
- Genetic Algorithm based Weights Optimization of Artificial Neural Network
- Artificial Neural Network Weights Optimization based on Imperialist Competitive Algorithm
- Genetic Algorithm based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection
- Using genetic algorithms to select architecture of a feedforward artificial neural network
- Applying Genetic Algorithm in Architecture and Neural Network Training
- Combining Genetic Algorithms and Neural Networks: The Encoding Problem – PhD Thesis – Philipp Koehn
- Deep Convex Net: A Scalable Architecture for Speech Pattern Classification
- Learning Stochastic Feedforward Neural Networks
- A New Learning Algorithm for Stochastic Feedforward Neural Nets
- Sequential Neural Models with Stochastic Layers
- Efficient BackProp
- THE INCREDIBLE SHRINKING NEURAL NETWORK: NEW PERSPECTIVES ON LEARNING REPRESENTATIONS THROUGH THE LENS OF PRUNING
- Optimization for Training Deep Models – slides
- Training of neural networks
- A stochastic training model for perceptron algorithms
- Sum-Product Networks: A New Deep Architecture
- Understanding Deep Neural Networks with Rectified Linear Units
- How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks
- Training Feedforward Neural Networks Using Genetic Algorithms