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Found 18 papers, 4 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.
Sample-Efficient Clustering and Conquer Procedures for Parallel Large-Scale Ranking and Selection
We propose novel "clustering and conquer" procedures for the parallel large-scale ranking and selection (R&S) problem, which leverage correlation information for clustering to break the bottleneck of sample efficiency.
AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems
We introduce AlphaRank, an artificial intelligence approach to address the fixed-budget ranking and selection (R&S) problems.
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.
Efficient Learning for Selecting Top-m Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts.
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.
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search
We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decision process.
Quantile-Based Policy Optimization for Reinforcement Learning
Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards.
Pathological Image Segmentation with Noisy Labels
Segmentation of pathological images is essential for accurate disease diagnosis.
Noise Optimization for Artificial Neural Networks
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work.
Context-dependent Ranking and Selection under a Bayesian Framework
We consider a context-dependent ranking and selection problem.
Methodology
Efficient Learning for Clustering and Optimizing Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent decision-making.
Decision Making
Methodology
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,. e. g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions.
Ranking and Selection as Stochastic Control
Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation.
