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Found 11 papers, 0 papers with code
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information
The problem of how to take the right actions to make profits in sequential process continues to be difficult due to the quick dynamics and a significant amount of uncertainty in many application scenarios.
Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling
The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step.
Continuous-time q-learning for mean-field control problems
This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in the setting of entropy-regularized reinforcement learning.
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing.
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
Thompson Sampling on Asymmetric $α$-Stable Bandits
In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
This paper proposes a framework of localized training and decentralized execution to study MARL with network of states.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
MFFW: A new dataset for multi-focus image fusion
It is found that current methods are evaluated on simulated image sets or Lytro dataset.
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality.
Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
The dynamic setting allows us to consider time varying ambiguity sets, which include the cases where the drift and correlation are estimated on a rolling window of historical data or when the investor takes into account learning on the ambiguity.
