Search
Search Results
-
RETRACTED ARTICLE: AHI: a hybrid machine learning model for complex industrial information systems
A summary of the numerous hybrid machine learning (HML) patterns is provided in this paper, which covers the complete ML lifecycle from model...
-
Applications of Fokker Planck Equations in Machine Learning Algorithms
As the continuous limit of the gradient-based optimization algorithms, Fokker Planck (FP) equation can provide a qualitative description of the...
-
ADAM: a Model of Artificial Psyche
AbstractAn ADAM artificial psyche model implementing a hierarchical deep reinforcement learning architecture is proposed. ADAM is able to learn...
-
MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound
Mixed integer linear programming (MILP) is an NP-hard problem, which can be solved by the branch and bound algorithm by dividing the original problem...
-
Access Control Method for EV Charging Stations Based on State Aggregation and Q-Learning
This paper presents intelligent access control for a charging station and a framework for dynamically and adaptively managing charging requests from...
-
On the locality of the natural gradient for learning in deep Bayesian networks
We study the natural gradient method for learning in deep Bayesian networks, including neural networks. There are two natural geometries associated...
-
A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning
The fog/edge computing paradigm has been proposed to tackle the challenges inherent to the Internet of Things realm. Timely response, bandwidth...
-
A Parallel Approach to Advantage Actor Critic in Deep Reinforcement Learning
Deep Reinforcement learning (DRL) algorithms recently still take a long time to train models in many applications. Parallelization has the potential...
-
A Machine Learning Framework for Geodesics Under Spherical Wasserstein–Fisher–Rao Metric and Its Application for Weighted Sample Generation
Wasserstein–Fisher–Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon measures, which takes into account both...
-
A Survey of Advances in Multimodal Federated Learning with Applications
Data privacy has long been an item of emphasis for personal data. This is especially true for healthcare data, which is often multimodal (i.e., it...
-
Optimization for Deep Learning: An Overview
Optimization is a critical component in deep learning. We think optimization for neural networks is an interesting topic for theoretical research due...
-
Using Deep Neural Networks for Detecting Spurious Oscillations in Discontinuous Galerkin Solutions of Convection-Dominated Convection–Diffusion Equations
Standard discontinuous Galerkin finite element solutions to convection-dominated convection–diffusion equations usually possess sharp layers but also...
-
Deep Reinforcement Learning for Intelligent Migration of Fog Services in Smart Cities
Fog computing plays a crucial role in future smart city applications, enabling services running along the cloud-to-thing continuum with low latency...
-
Gradient-based algorithms for multi-objective bi-level optimization
Multi-objective bi-level optimization (MOBLO) addresses nested multi-objective optimization problems common in a range of applications. However, its...
-
An Algorithm Is Described for Predicting the Probability of Success of Signal Transmission in a Wireless Communication System Using Machine Learning
AbstractA dynamic machine learning algorithm is described for predicting the probability of successful signal transmission and adaptive signal...
-
Learning-Based Control for Hybrid Battery Management Systems
Battery packs of electric vehicles are prone to capacity, thermal, and aging imbalances in their cells, which limit power delivery to the vehicle. To...
-
Generative Models and Unsupervised Learning
The last part of our voyage toward the understanding of the geometry of deep learning concerns perhaps the most exciting aspect of deep...
-
Reinforcement Learning-Based Planning and Control
While optimization-based approaches still enjoy mainstream appeal in solving motion planning and control problems, learning-based approaches have...
-
Maximum Independent Sets and Supervised Learning
The paper discusses an enhancement to a recently presented supervised learning algorithm to solve the Maximum Independent Set problem. In particular,...
