Pessimism in the face of confounders: Provably efficient offline reinforcement learning in partially observable markov decision processes
We study offline reinforcement learning (RL) in partially observable Markov decision
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …
Quadratic residual multiplicative filter neural networks for efficient approximation of complex sensor signals
MU Demirezen - IEEE Access, 2023 - ieeexplore.ieee.org
In this research, we present an innovative Quadratic Residual Multiplicative Filter Neural
Network (QRMFNN) to effectively learn extremely complex sensor signals as a low …
Network (QRMFNN) to effectively learn extremely complex sensor signals as a low …
Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions
Blind deconvolution (BD) has been demonstrated to be an efficacious approach for
extracting bearing fault-specific features from vibration signals under strong background …
extracting bearing fault-specific features from vibration signals under strong background …
Polyhedron attention module: learning adaptive-order interactions
Learning feature interactions can be the key for multivariate predictive modeling. ReLU-
activated neural networks create piecewise linear prediction models, and other nonlinear …
activated neural networks create piecewise linear prediction models, and other nonlinear …
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
Deep learning has achieved remarkable success in bearing fault diagnosis. However, its
performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed …
performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed …
Cloud-rain: point cloud analysis with reflectional invariance
The networks for point cloud tasks are expected to be invariant when the point clouds are
affinely transformed such as rotation and reflection. So far, relative to the rotational …
affinely transformed such as rotation and reflection. So far, relative to the rotational …
Quadratic graph attention network (Q-GAT) for robust construction of gene regulatory networks
Gene regulatory relationships can be abstracted as a gene regulatory network (GRN), which
plays a key role in characterizing complex cellular processes and pathways. Recently, graph …
plays a key role in characterizing complex cellular processes and pathways. Recently, graph …
Infinite-Dimensional Feature Interaction
The past neural network design has largely focused on feature representation space
dimension and its capacity scaling (eg, width, depth), but overlooked the feature interaction …
dimension and its capacity scaling (eg, width, depth), but overlooked the feature interaction …