Search Results for author:
Found 11 papers, 0 papers with code
Lightweight Monocular Depth Estimation with an Edge Guided Network
In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA).
Latent Preserving Generative Adversarial Network for Imbalance classification
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming
Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction.
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification.
Does Adversarial Oversampling Help us?
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces
Since the real conditional distribution of data is ignored, the clustering inference network can only achieve inferior clustering performance by considering only uniform prior based generative samples.
Q-Learning with Differential Entropy of Q-Tables
It is well-known that information loss can occur in the classic and simple Q-learning algorithm.
Towards Interpretable ANNs: An Exact Transformation to Multi-Class Multivariate Decision Trees
While the EC-DT translates an ANN in a layer-wise manner to represent exactly the decision boundaries implicitly learned by the hidden layers of the network, the Extended C-Net combines the decompositional approach from EC-DT with a C5 tree learning algorithm to form decision rules.
Machine Education: Designing semantically ordered and ontologically guided modular neural networks
The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning.
Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines
The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing.
A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data
Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently.
