0000000000264432
AUTHOR
Jukka K. Nurminen
showing 3 related works from this author
Node co-activations as a means of error detection : Towards fault-tolerant neural networks
2022
Context: Machine learning has proved an efficient tool, but the systems need tools to mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors before they cause harm. Objective: This paper investigates whether rare co-activations – pairs of usually segregated nodes activating together – are indicative of problems in neural networks (NN). These could be used to detect concept drift and flagging untrustworthy predictions. Method: We trained four NNs. For each, we studied how often each pair of nodes activates together. In a separate test set, we counted how many rare co-activations occurred with each input, and grouped the inputs based on whether its class…
Mobile Search - Social Network Search Using Mobile Devices
2008
During the last years progress in Web search engines has been made to the point that relevant information can be reached easily most of the time. However very little empirical research has been carried to study Web search in highly dynamic social network environments composed of mobile devices. The aim of this work was therefore to investigate novel approaches that took advantage of the social network environment inherent to mobile peer-to-peer paradigm. The work focused mainly on the development of a prototype for mobile search concept. The prototype was built on top of Drupal content site management system. This study suggests that the methods presented can be a complement to traditional …
Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations
2022
Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence engineering approaches, but practices and tools are still needed for the testing and monitoring of ML-enabled systems. Peer reviewed