33,737 research outputs found
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
Audio-based music classification with a pretrained convolutional network
Recently the ‘Million Song Dataset’, containing audio features and metadata for one million songs, was made available. In this paper, we build a convolutional network that is then trained to perform artist recognition, genre recognition and key detection. The network is tailored to summarize the audio features over musically significant timescales. It is infeasible to train the network on all available data in a supervised fashion, so we use unsupervised pretraining to be able to harness the entire dataset: we train a convolutional deep belief network on all data, and then use the learnt parameters to initialize a convolutional multilayer perceptron with the same architecture. The MLP is then trained on a labeled subset of the data for each task. We also train the same MLP with randomly initialized weights. We find that our convolutional approach improves accuracy for the genre recognition and artist recognition tasks. Unsupervised pretraining improves convergence speed in all cases. For artist recognition it improves accuracy as well
Network Model Selection Using Task-Focused Minimum Description Length
Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented. Users of downstream network data may not even be
aware of these choices or their impacts. We propose a task-focused network
model selection methodology which addresses several key challenges. Our
approach constructs network models from underlying data and uses minimum
description length (MDL) criteria for selection. Our methodology measures
efficiency, a general and comparable measure of the network's performance of a
local (i.e. node-level) predictive task of interest. Selection on efficiency
favors parsimonious (e.g. sparse) models to avoid overfitting and can be
applied across arbitrary tasks and representations. We show stability,
sensitivity, and significance testing in our methodology
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