419 research outputs found
On the Parameterized Complexity of Learning Monadic Second-Order Formulas
Within the model-theoretic framework for supervised learning introduced by
Grohe and Tur\'an (TOCS 2004), we study the parameterized complexity of
learning concepts definable in monadic second-order logic (MSO). We show that
the problem of learning a consistent MSO-formula is fixed-parameter tractable
on structures of bounded tree-width and on graphs of bounded clique-width in
the 1-dimensional case, that is, if the instances are single vertices (and not
tuples of vertices). This generalizes previous results on strings and on trees.
Moreover, in the agnostic PAC-learning setting, we show that the result also
holds in higher dimensions. Finally, via a reduction to the MSO-model-checking
problem, we show that learning a consistent MSO-formula is para-NP-hard on
general structures
Recent Advances in Fully Dynamic Graph Algorithms
In recent years, significant advances have been made in the design and
analysis of fully dynamic algorithms. However, these theoretical results have
received very little attention from the practical perspective. Few of the
algorithms are implemented and tested on real datasets, and their practical
potential is far from understood. Here, we present a quick reference guide to
recent engineering and theory results in the area of fully dynamic graph
algorithms
Contextualize Me -- The Case for Context in Reinforcement Learning
While Reinforcement Learning ( RL) has made great strides towards solving
increasingly complicated problems, many algorithms are still brittle to even
slight environmental changes. Contextual Reinforcement Learning (cRL) provides
a framework to model such changes in a principled manner, thereby enabling
flexible, precise and interpretable task specification and generation. Our goal
is to show how the framework of cRL contributes to improving zero-shot
generalization in RL through meaningful benchmarks and structured reasoning
about generalization tasks. We confirm the insight that optimal behavior in cRL
requires context information, as in other related areas of partial
observability. To empirically validate this in the cRL framework, we provide
various context-extended versions of common RL environments. They are part of
the first benchmark library, CARL, designed for generalization based on cRL
extensions of popular benchmarks, which we propose as a testbed to further
study general agents. We show that in the contextual setting, even simple RL
environments become challenging - and that naive solutions are not enough to
generalize across complex context spaces.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0210
Student Expectations: The effect of student background and experience
CONTEXT
The perspectives and previous experiences that students bring to their programs of study can affect their approaches to study and the depth of learning that they achieve Prosser & Trigwell, 1999; Ramsden, 2003). Graduate outcomes assume the attainment of welldeveloped independent learning skills which can be transferred to the work-place.
PURPOSE
This 5-year longitudinal study investigates factors influencing students’ approaches to learning in the fields of Engineering, Software Engineering, and Computer Science, at two higher education institutes delivering programs of various levels in Australia and New Zealand. The study aims to track the development of student approaches to learning as they progress through their program. Through increased understanding of students’ approaches, faculty will be better able to design teaching and learning strategies to meet the needs of an increasingly diverse student body. This paper reports on the first stage of the project.
APPROACH
In August 2017, we ran a pilot of our survey using the Revised Study Process Questionnaire(Biggs, Kember, & Leung, 2001) and including some additional questions related to student demographics and motivation for undertaking their current program of study. Data were analysed to evaluate the usefulness of data collected and to understand the demographics of the student cohort. Over the period of the research, data will be collected using the questionnaire and through focus groups and interviews.
RESULTS
Participants provided a representative sample, and the data collected was reasonable, allowing the questionnaire design to be confirmed.
CONCLUSIONS
At this preliminary stage, the study has provided insight into the student demographics at both institutes and identified aspects of students’ modes of engagement with learning. Some areas for improvement of the questionnaire have been identified, which will be implemented for the main body of the study
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Inspired by the success of deploying deep learning in the fields of Computer
Vision and Natural Language Processing, this learning paradigm has also found
its way into the field of Music Information Retrieval. In order to benefit from
deep learning in an effective, but also efficient manner, deep transfer
learning has become a common approach. In this approach, it is possible to
reuse the output of a pre-trained neural network as the basis for a new
learning task. The underlying hypothesis is that if the initial and new
learning tasks show commonalities and are applied to the same type of input
data (e.g. music audio), the generated deep representation of the data is also
informative for the new task. Since, however, most of the networks used to
generate deep representations are trained using a single initial learning
source, their representation is unlikely to be informative for all possible
future tasks. In this paper, we present the results of our investigation of
what are the most important factors to generate deep representations for the
data and learning tasks in the music domain. We conducted this investigation
via an extensive empirical study that involves multiple learning sources, as
well as multiple deep learning architectures with varying levels of information
sharing between sources, in order to learn music representations. We then
validate these representations considering multiple target datasets for
evaluation. The results of our experiments yield several insights on how to
approach the design of methods for learning widely deployable deep data
representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications:
Special Issue on Deep Learning for Music and Audio
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