2,778 research outputs found
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
We propose a framework for interactive and explainable machine learning that
enables users to (1) understand machine learning models; (2) diagnose model
limitations using different explainable AI methods; as well as (3) refine and
optimize the models. Our framework combines an iterative XAI pipeline with
eight global monitoring and steering mechanisms, including quality monitoring,
provenance tracking, model comparison, and trust building. To operationalize
the framework, we present explAIner, a visual analytics system for interactive
and explainable machine learning that instantiates all phases of the suggested
pipeline within the commonly used TensorBoard environment. We performed a
user-study with nine participants across different expertise levels to examine
their perception of our workflow and to collect suggestions to fill the gap
between our system and framework. The evaluation confirms that our tightly
integrated system leads to an informed machine learning process while
disclosing opportunities for further extensions.Comment: 9 pages paper, 2 pages references, 5 pages supplementary material
(ancillary files
Rethinking affordance
n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory
On data skewness, stragglers, and MapReduce progress indicators
We tackle the problem of predicting the performance of MapReduce
applications, designing accurate progress indicators that keep programmers
informed on the percentage of completed computation time during the execution
of a job. Through extensive experiments, we show that state-of-the-art progress
indicators (including the one provided by Hadoop) can be seriously harmed by
data skewness, load unbalancing, and straggling tasks. This is mainly due to
their implicit assumption that the running time depends linearly on the input
size. We thus design a novel profile-guided progress indicator, called
NearestFit, that operates without the linear hypothesis assumption and exploits
a careful combination of nearest neighbor regression and statistical curve
fitting techniques. Our theoretical progress model requires fine-grained
profile data, that can be very difficult to manage in practice. To overcome
this issue, we resort to computing accurate approximations for some of the
quantities used in our model through space- and time-efficient data streaming
algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive
empirical assessment over the Amazon EC2 platform on a variety of real-world
benchmarks shows that NearestFit is practical w.r.t. space and time overheads
and that its accuracy is generally very good, even in scenarios where
competitors incur non-negligible errors and wide prediction fluctuations.
Overall, NearestFit significantly improves the current state-of-art on progress
analysis for MapReduce
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
During neurosurgery, medical images of the brain are used to locate tumors
and critical structures, but brain tissue shifts make pre-operative images
unreliable for accurate removal of tumors. Intra-operative imaging can track
these deformations but is not a substitute for pre-operative data. To address
this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and
time-consuming image processing operation that adjusts the pre-operative image
data to account for intra-operative brain shift. Our review explores a specific
NRR method for registering brain MRI during image-guided neurosurgery and
examines various strategies for improving the accuracy and speed of the NRR
method. We demonstrate that our implementation enables NRR results to be
delivered within clinical time constraints while leveraging Distributed
Computing and Machine Learning to enhance registration accuracy by identifying
optimal parameters for the NRR method. Additionally, we highlight challenges
associated with its use in the operating room
Validating one-class active learning with user studies – A prototype and open challenges
Active learning with one-class classifiers involves users in the detection of outliers. The evaluation of one-class active learning typically relies on user feedback that is simulated, based on benchmark data. This is because validations with real users are elaborate. They require the de-sign and implementation of an interactive learning system. But without such a validation, it is unclear whether the value proposition of active learning does materialize when it comes to an actual detection of out-liers. User studies are necessary to find out when users can indeed provide feedback. In this article, we describe important characteristics and pre-requisites of one-class active learning for outlier detection, and how they influence the design of interactive systems. We propose a reference architecture of a one-class active learning system. We then describe design alternatives regarding such a system and discuss conceptual and technical challenges. We conclude with a roadmap towards validating one-class active learning with user studies
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