Search Results for author:
Found 6 papers, 2 papers with code
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
In this paper, we use a recently proposed nonlinear decision-tree (NLDT) approach to find a hierarchical set of control rules in an attempt to maximize the open-loop performance for approximating and explaining the pre-trained black-box DRL (oracle) agent using the labelled state-action dataset.
Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods
Classification of datasets into two or more distinct classes is an important machine learning task.
Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems
By restricting the structure of split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is assured.
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.
Ranked #1 on
Pneumonia Detection
on ChestX-ray14
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS).
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS).
