13,865 research outputs found
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Building behavior profiles of Android applications (apps) with holistic, rich
and multi-view information (e.g., incorporating several semantic views of an
app such as API sequences, system calls, etc.) would help catering downstream
analytics tasks such as app categorization, recommendation and malware analysis
significantly better. Towards this goal, we design a semi-supervised
Representation Learning (RL) framework named apk2vec to automatically generate
a compact representation (aka profile/embedding) for a given app. More
specifically, apk2vec has the three following unique characteristics which make
it an excellent choice for largescale app profiling: (1) it encompasses
information from multiple semantic views such as API sequences, permissions,
etc., (2) being a semi-supervised embedding technique, it can make use of
labels associated with apps (e.g., malware family or app category labels) to
build high quality app profiles, and (3) it combines RL and feature hashing
which allows it to efficiently build profiles of apps that stream over time
(i.e., online learning). The resulting semi-supervised multi-view hash
embeddings of apps could then be used for a wide variety of downstream tasks
such as the ones mentioned above. Our extensive evaluations with more than
42,000 apps demonstrate that apk2vec's app profiles could significantly
outperform state-of-the-art techniques in four app analytics tasks namely,
malware detection, familial clustering, app clone detection and app
recommendation.Comment: International Conference on Data Mining, 201
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
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A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
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