21 citations · 50 across the 14 of their papers we have counts for
14 papers
Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
Akshay Murthy, Shawn Sebastian, Manil Shangle +3
Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff b…
Superfast Selection for Decision Tree Algorithms
Huaduo Wang, Gopal Gupta
We present a novel and systematic method, called Superfast Selection, for selecting the "optimal split" for decision tree and feature selection algorithms over tabular data. The me…
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Parth Padalkar, Natalia Ślusarz, Ekaterina Komendantskaya +1
Recent efforts in interpreting Convolutional Neural Networks (CNNs) focus on translating the activation of CNN filters into a stratified Answer Set Program (ASP) rule-sets. The CNN…
Counterfactual Generation with Answer Set Programming
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail approval, hiring, and many more. Unfo…
Counterfactual Explanation Generation with s(CASP)
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail, hiring, and many more. Unfortunately…
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks
Parth Padalkar, Gopal Gupta
Within the realm of deep learning, the interpretability of Convolutional Neural Networks (CNNs), particularly in the context of image classification tasks, remains a formidable cha…
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
Yankai Zeng, Abhiramon Rajasekharan, Parth Padalkar +3
Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These…
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming
Abhiramon Rajasekharan, Yankai Zeng, Parth Padalkar +1
Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions.…
NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification
Parth Padalkar, Huaduo Wang, Gopal Gupta
Deep learning models such as CNNs have surpassed human performance in computer vision tasks such as image classification. However, despite their sophistication, these models lack i…
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability
Huaduo Wang, Gopal Gupta
We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. FOLD-SE generates…
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
Huaduo Wang, Gopal Gupta
FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data.…
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
Huaduo Wang, Farhad Shakerin, Gopal Gupta
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (AS…
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning
Suraj Kothawade, Vinaya Khandelwal, Kinjal Basu +2
Driving an automobile involves the tasks of observing surroundings, then making a driving decision based on these observations (steer, brake, coast, etc.). In autonomous driving, a…
CASPR: A Commonsense Reasoning-based Conversational Socialbot
Kinjal Basu, Huaduo Wang, Nancy Dominguez +4
We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is tha…