<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Error-margin Analysis for Hidden Neuron Activation Labels</style></title><secondary-title><style face="normal" font="default" size="100%">18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CNN</style></keyword><keyword><style  face="normal" font="default" size="100%">Concept Induction</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable AI</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer </style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding how high-level concepts are represented with- in artificial neural networks is a fundamental challenge in the field of arti- ficial intelligence. While existing literature in explainable AI emphasizes the importance of labeling neurons with concepts to understand their functioning, they mostly focus on identifying what stimulus activates a neuron in most cases; this corresponds to the notion of recall in informa- tion retrieval. We argue that this is only the first-part of a two-part job; it is imperative to also investigate neuron responses to other stimuli, i.e., their precision. We call this the neuron label’s error margin.&lt;/p&gt;
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