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Found 8 papers, 1 papers with code
Forward Learning for Gradient-based Black-box Saliency Map Generation
Gradient-based saliency maps are widely used to explain deep neural network decisions.
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation.
Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation.
RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search
Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space.
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines.
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms
Classical reinforcement learning (RL) aims to optimize the expected cumulative reward.
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
Quantile-Based Policy Optimization for Reinforcement Learning
Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards.
