19,035 research outputs found
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv
version contains two identical sub-figure
Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Graph-based Semi-supervised learning (SSL) algorithms have been successfully
used in a large number of applications. These methods classify initially
unlabeled nodes by propagating label information over the structure of graph
starting from seed nodes. Graph-based SSL algorithms usually scale linearly
with the number of distinct labels (m), and require O(m) space on each node.
Unfortunately, there exist many applications of practical significance with
very large m over large graphs, demanding better space and time complexity. In
this paper, we propose MAD-SKETCH, a novel graph-based SSL algorithm which
compactly stores label distribution on each node using Count-min Sketch, a
randomized data structure. We present theoretical analysis showing that under
mild conditions, MAD-SKETCH can reduce space complexity at each node from O(m)
to O(log m), and achieve similar savings in time complexity as well. We support
our analysis through experiments on multiple real world datasets. We observe
that MAD-SKETCH achieves similar performance as existing state-of-the-art
graph- based SSL algorithms, while requiring smaller memory footprint and at
the same time achieving up to 10x speedup. We find that MAD-SKETCH is able to
scale to datasets with one million labels, which is beyond the scope of
existing graph- based SSL algorithms.Comment: 9 page
Optimal Transport for Domain Adaptation
Domain adaptation from one data space (or domain) to another is one of the
most challenging tasks of modern data analytics. If the adaptation is done
correctly, models built on a specific data space become more robust when
confronted to data depicting the same semantic concepts (the classes), but
observed by another observation system with its own specificities. Among the
many strategies proposed to adapt a domain to another, finding a common
representation has shown excellent properties: by finding a common
representation for both domains, a single classifier can be effective in both
and use labelled samples from the source domain to predict the unlabelled
samples of the target domain. In this paper, we propose a regularized
unsupervised optimal transportation model to perform the alignment of the
representations in the source and target domains. We learn a transportation
plan matching both PDFs, which constrains labelled samples in the source domain
to remain close during transport. This way, we exploit at the same time the few
labeled information in the source and the unlabelled distributions observed in
both domains. Experiments in toy and challenging real visual adaptation
examples show the interest of the method, that consistently outperforms state
of the art approaches
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i.e., sets of terms referring to the same entity)
is an important task for many entity-leveraging applications. Previous work
either rank terms based on their similarity to a given query term, or treats
the problem as a two-phase task (i.e., detecting synonymy pairs, followed by
organizing these pairs into synonym sets). However, these approaches fail to
model the holistic semantics of a set and suffer from the error propagation
issue. Here we propose a new framework, named SynSetMine, that efficiently
generates entity synonym sets from a given vocabulary, using example sets from
external knowledge bases as distant supervision. SynSetMine consists of two
novel modules: (1) a set-instance classifier that jointly learns how to
represent a permutation invariant synonym set and whether to include a new
instance (i.e., a term) into the set, and (2) a set generation algorithm that
enumerates the vocabulary only once and applies the learned set-instance
classifier to detect all entity synonym sets in it. Experiments on three real
datasets from different domains demonstrate both effectiveness and efficiency
of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201
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