2,108 research outputs found
Decontextualized learning for interpretable hierarchical representations of visual patterns
Apart from discriminative models for classification and object detection
tasks, the application of deep convolutional neural networks to basic research
utilizing natural imaging data has been somewhat limited; particularly in cases
where a set of interpretable features for downstream analysis is needed, a key
requirement for many scientific investigations. We present an algorithm and
training paradigm designed specifically to address this: decontextualized
hierarchical representation learning (DHRL). By combining a generative model
chaining procedure with a ladder network architecture and latent space
regularization for inference, DHRL address the limitations of small datasets
and encourages a disentangled set of hierarchically organized features. In
addition to providing a tractable path for analyzing complex hierarchal
patterns using variation inference, this approach is generative and can be
directly combined with empirical and theoretical approaches. To highlight the
extensibility and usefulness of DHRL, we demonstrate this method in application
to a question from evolutionary biology
Discriminative models for multi-instance problems with tree-structure
Modeling network traffic is gaining importance in order to counter modern
threats of ever increasing sophistication. It is though surprisingly difficult
and costly to construct reliable classifiers on top of telemetry data due to
the variety and complexity of signals that no human can manage to interpret in
full. Obtaining training data with sufficiently large and variable body of
labels can thus be seen as prohibitive problem. The goal of this work is to
detect infected computers by observing their HTTP(S) traffic collected from
network sensors, which are typically proxy servers or network firewalls, while
relying on only minimal human input in model training phase. We propose a
discriminative model that makes decisions based on all computer's traffic
observed during predefined time window (5 minutes in our case). The model is
trained on collected traffic samples over equally sized time window per large
number of computers, where the only labels needed are human verdicts about the
computer as a whole (presumed infected vs. presumed clean). As part of training
the model itself recognizes discriminative patterns in traffic targeted to
individual servers and constructs the final high-level classifier on top of
them. We show the classifier to perform with very high precision, while the
learned traffic patterns can be interpreted as Indicators of Compromise. In the
following we implement the discriminative model as a neural network with
special structure reflecting two stacked multi-instance problems. The main
advantages of the proposed configuration include not only improved accuracy and
ability to learn from gross labels, but also automatic learning of server types
(together with their detectors) which are typically visited by infected
computers
Stealth Assessment of Self-Regulative Behaviors within a Game-Based Environment
abstract: Students' ability to regulate and control their behaviors during learning has been shown to be a critical skill for academic success. However, researchers often struggle with ways to capture the nuances of this ability, often solely relying on self-report measures. This thesis proposal employs a novel approach to investigating variations in students' ability to self-regulate by using process data from the game-based Intelligent Tutoring System (ITS) iSTART-ME. This approach affords a nuanced examination of how students' regulate their interactions with game-based features at both a coarse-grained and fine-grain levels and the ultimate impact that those behaviors have on in-system performance and learning outcomes (i.e., self-explanation quality). This thesis is comprised of two submitted manuscripts that examined how a group of 40 high school students chose to engage with game-based features and how those interactions influenced their target skill performance. Findings suggest that in-system log data has the potential to provide stealth assessments of students' self-regulation while learning.Dissertation/ThesisM.A. Psychology 201
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