Scalable inverse reinforcement learning through multifidelity Bayesian optimization
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
[HTML][HTML] Mismatch between tissue partial oxygen pressure and near-infrared spectroscopy neuromonitoring of tissue respiration in acute brain trauma: the rationale for …
The brain tissue partial oxygen pressure (PbtO2) and near-infrared spectroscopy (NIRS)
neuromonitoring are frequently compared in the management of acute moderate and severe …
neuromonitoring are frequently compared in the management of acute moderate and severe …
Optimal synchronization control of heterogeneous asymmetric input-constrained unknown nonlinear MASs via reinforcement learning
The asymmetric input-constrained optimal synchronization problem of heterogeneous
unknown nonlinear multiagent systems (MASs) is considered in the paper. Intuitively, a state …
unknown nonlinear multiagent systems (MASs) is considered in the paper. Intuitively, a state …
Graph-based Bayesian optimization for large-scale objective-based experimental design
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Design is an inseparable part of most scientific and engineering tasks, including real and
simulation-based experimental design processes and parameter/hyperparameter …
simulation-based experimental design processes and parameter/hyperparameter …
A deep learning model for identification of diabetes type 2 based on nucleotide signals
B Das - Neural Computing and Applications, 2022 - Springer
Abstract In Genome-Wide Association Studies (GWAS), detection of T2D-related variants in
genome sequences and accurate modeling of the complex structure of the relevant gene are …
genome sequences and accurate modeling of the complex structure of the relevant gene are …
[HTML][HTML] Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach
Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity
comprised of heterogenous syndromes affecting both the peripheral and central nervous …
comprised of heterogenous syndromes affecting both the peripheral and central nervous …
Reinforcement learning data-acquiring for causal inference of regulatory networks
Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities
govern various cellular processes. The limitations in genomics data and the complexity of …
govern various cellular processes. The limitations in genomics data and the complexity of …
Evidential reasoning rule with likelihood analysis and perturbation analysis
SW Tang, ZJ Zhou, GY Hu, Y Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The evidential reasoning (ER) rule has been widely used in the data analysis, which
provides a transparent and credible inference process and can effectively deal with various …
provides a transparent and credible inference process and can effectively deal with various …
Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks
The complexity, scale, and uncertainty in regulatory networks (eg, gene regulatory networks
and microbial networks) regularly pose a huge uncertainty in their models. These …
and microbial networks) regularly pose a huge uncertainty in their models. These …
Bayesian surrogate learning for uncertainty analysis of coupled multidisciplinary systems
SF Ghoreishi, M Imani - Journal of Computing and …, 2021 - asmedigitalcollection.asme.org
Engineering systems are often composed of many subsystems that interact with each other.
These subsystems, referred to as disciplines, contain many types of uncertainty and in many …
These subsystems, referred to as disciplines, contain many types of uncertainty and in many …