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Found 9 papers, 2 papers with code
Stock market forecasting using DRAGAN and feature matching
GANs are powerful generative models successfully applied in different areas but suffer from inherent challenges such as training instability and mode collapse.
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition
In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output.
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction
MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state.
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization
Trust Region Policy Optimization (TRPO) is a popular and empirically successful policy search algorithm in reinforcement learning (RL).
Modified Double DQN: addressing stability
Therefore, in this paper three modifications to the Double-DQN algorithm are proposed with the hope of maintaining the performance in the terms of both stability and overestimation.
Maximum Entropy Dueling Network Architecture in Atari Domain
In recent years, there have been many deep structures for Reinforcement Learning, mainly for value function estimation and representations.
Adaptive Explicit Kernel Minkowski Weighted K-means
These explicit feature maps enable us to access the data in the feature space explicitly and take advantage of K-means extensions in that space.
Backpropagation-Free Learning Method for Correlated Fuzzy Neural Networks
This learning approach does not require backpropagating the output error to learn the premise parts' parameters.
A self-organizing fuzzy neural network for sequence learning
The proposed method is used to learn and reproduce different sequences simultaneously which is the novelty of this method.
