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Title: Interactive Machine Learning at Scale with CHISSL

Abstract

We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capa- ble of incorporating user feedback incrementally and imme- diately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deter- ministically, with O(n) space and time complexity. Our al- gorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1440623
Report Number(s):
PNNL-SA-129748
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), February 2-7, 2018, New Orleans, Louisiana, 8194-8195
Country of Publication:
United States
Language:
English

Citation Formats

Arendt, Dustin L., Grace, Emily A., and Volkova, Svitlana. Interactive Machine Learning at Scale with CHISSL. United States: N. p., 2018. Web.
Arendt, Dustin L., Grace, Emily A., & Volkova, Svitlana. Interactive Machine Learning at Scale with CHISSL. United States.
Arendt, Dustin L., Grace, Emily A., and Volkova, Svitlana. Wed . "Interactive Machine Learning at Scale with CHISSL". United States. doi:.
@article{osti_1440623,
title = {Interactive Machine Learning at Scale with CHISSL},
author = {Arendt, Dustin L. and Grace, Emily A. and Volkova, Svitlana},
abstractNote = {We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capa- ble of incorporating user feedback incrementally and imme- diately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deter- ministically, with O(n) space and time complexity. Our al- gorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Feb 07 00:00:00 EST 2018},
month = {Wed Feb 07 00:00:00 EST 2018}
}

Conference:
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