21 citations · 36 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework
Alakh Aggarwal, Rishita Bansal, Parth Padalkar +1
These days automation is being applied everywhere. In every environment, planning for the actions to be taken by the agents is an important aspect. In this paper, we plan to implem…
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…