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Found 7 papers, 3 papers with code
Error Controlled Actor-Critic
On error of value function inevitably causes an overestimation phenomenon and has a negative impact on the convergence of the algorithms.
Error Controlled Actor-Critic Method to Reinforcement Learning
In the reinforcement learning (RL) algorithms which incorporate function approximation methods, the approximation error of value function inevitably cause overestimation phenomenon and have a negative impact on the convergence of the algorithms.
Task Augmentation by Rotating for Meta-Learning
Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning.
Decoder Choice Network for Meta-Learning
This work proposes a method that controls the gradient descent process of the model parameters of a neural network by limiting the model parameters in a low-dimensional latent space.
Stock Prices Prediction using Deep Learning Models
The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.
Gradient Boost with Convolution Neural Network for Stock Forecast
Market economy closely connects aspects to all walks of life.
