721 research outputs found
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Originally designed to model text, topic modeling has become a powerful tool
for uncovering latent structure in domains including medicine, finance, and
vision. The goals for the model vary depending on the application: in some
cases, the discovered topics may be used for prediction or some other
downstream task. In other cases, the content of the topic itself may be of
intrinsic scientific interest.
Unfortunately, even using modern sparse techniques, the discovered topics are
often difficult to interpret due to the high dimensionality of the underlying
space. To improve topic interpretability, we introduce Graph-Sparse LDA, a
hierarchical topic model that leverages knowledge of relationships between
words (e.g., as encoded by an ontology). In our model, topics are summarized by
a few latent concept-words from the underlying graph that explain the observed
words. Graph-Sparse LDA recovers sparse, interpretable summaries on two
real-world biomedical datasets while matching state-of-the-art prediction
performance
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its inference easily by connecting sub-modules to allow
the robots to acquire various capabilities through interaction with their
environments and others. We consider that large-scale cognitive models can be
constructed by connecting smaller fundamental models hierarchically while
maintaining their programmatic independence. Moreover, connected modules are
dependent on each other, and parameters are required to be optimized as a
whole. Conventionally, the equations for parameter estimation have to be
derived and implemented depending on the models. However, it becomes harder to
derive and implement those of a larger scale model. To solve these problems, in
this paper, we propose a method for parameter estimation by communicating the
minimal parameters between various modules while maintaining their programmatic
independence. Therefore, Serket makes it easy to construct large-scale models
and estimate their parameters via the connection of modules. Experimental
results demonstrated that the model can be constructed by connecting modules,
the parameters can be optimized as a whole, and they are comparable with the
original models that we have proposed
Hierarchical Multiclass Topic Modelling with Prior Knowledge
Eine neue Multi-Label-Dokument-Klassifizierungstechnik namens CascadeLDA wird in dieser Arbeit eingefĂĽhrt. Statt sich auf diskriminierende Modellierungstechniken zu konzentrieren, erweitert CascadeLDA ein generatives Basismodell durch die Einbeziehung von zwei Arten von Vorinformationen.
Erstens wird das Wissen aus einem gekennzeichneten Trainingsdatensatz verwendet, um das generative Modell zu steuern. Zweitens wird die implizite Baumstruktur der Labels ausgenutzt, um diskriminierende Eigenschaften zwischen eng verwandten Labels hervorzuheben. Durch die Transformation des Klassifizierungsproblems in einem
Ensemble von kleineren Problemen, werden vergleichbare out-of-sample Resultate circa 25 mal schneller erreicht als im Basismodell. In diesem Paper wird CascadeLDA auf Datensätzen mit akademischen Abstracts und vollständige wissenschaftliche angewendet. Das Modell wird eingesetzt, um Autoren beim Klassifizieren ihrer Publikationen automatisch zu unterstützen.A new multi-label document classification technique called CascadeLDA is introduced
in this thesis. Rather than focusing on discriminative modelling techniques, CascadeLDA
extends a baseline generative model by incorporating two types of prior information.
Firstly, knowledge from a labeled training dataset is used to direct the generative model.
Secondly, the implicit tree structure of the labels is exploited to emphasise discriminative
features between closely related labels. By segregating the classification problem in an
ensemble of smaller problems, out-of-sample results are achieved at about 25 times the
speed of the baseline model. In this thesis, CascadeLDA is performed on datasets with
academic abstracts and full academic papers. The model is employed to assist authors in
tagging their newly published articles
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