107,935 research outputs found
Anytime Hierarchical Clustering
We propose a new anytime hierarchical clustering method that iteratively
transforms an arbitrary initial hierarchy on the configuration of measurements
along a sequence of trees we prove for a fixed data set must terminate in a
chain of nested partitions that satisfies a natural homogeneity requirement.
Each recursive step re-edits the tree so as to improve a local measure of
cluster homogeneity that is compatible with a number of commonly used (e.g.,
single, average, complete) linkage functions. As an alternative to the standard
batch algorithms, we present numerical evidence to suggest that appropriate
adaptations of this method can yield decentralized, scalable algorithms
suitable for distributed/parallel computation of clustering hierarchies and
online tracking of clustering trees applicable to large, dynamically changing
databases and anomaly detection.Comment: 13 pages, 6 figures, 5 tables, in preparation for submission to a
conferenc
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Improving Variational Encoder-Decoders in Dialogue Generation
Variational encoder-decoders (VEDs) have shown promising results in dialogue
generation. However, the latent variable distributions are usually approximated
by a much simpler model than the powerful RNN structure used for encoding and
decoding, yielding the KL-vanishing problem and inconsistent training
objective. In this paper, we separate the training step into two phases: The
first phase learns to autoencode discrete texts into continuous embeddings,
from which the second phase learns to generalize latent representations by
reconstructing the encoded embedding. In this case, latent variables are
sampled by transforming Gaussian noise through multi-layer perceptrons and are
trained with a separate VED model, which has the potential of realizing a much
more flexible distribution. We compare our model with current popular models
and the experiment demonstrates substantial improvement in both metric-based
and human evaluations.Comment: Accepted by AAAI201
Modeling relation paths for knowledge base completion via joint adversarial training
Knowledge Base Completion (KBC), which aims at determining the missing
relations between entity pairs, has received increasing attention in recent
years. Most existing KBC methods focus on either embedding the Knowledge Base
(KB) into a specific semantic space or leveraging the joint probability of
Random Walks (RWs) on multi-hop paths. Only a few unified models take both
semantic and path-related features into consideration with adequacy. In this
paper, we propose a novel method to explore the intrinsic relationship between
the single relation (i.e. 1-hop path) and multi-hop paths between paired
entities. We use Hierarchical Attention Networks (HANs) to select important
relations in multi-hop paths and encode them into low-dimensional vectors. By
treating relations and multi-hop paths as two different input sources, we use a
feature extractor, which is shared by two downstream components (i.e. relation
classifier and source discriminator), to capture shared/similar information
between them. By joint adversarial training, we encourage our model to extract
features from the multi-hop paths which are representative for relation
completion. We apply the trained model (except for the source discriminator) to
several large-scale KBs for relation completion. Experimental results show that
our method outperforms existing path information-based approaches. Since each
sub-module of our model can be well interpreted, our model can be applied to a
large number of relation learning tasks.Comment: Accepted by Knowledge-Based System
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