906 research outputs found
A Meta-Learning Approach to One-Step Active Learning
We consider the problem of learning when obtaining the training labels is
costly, which is usually tackled in the literature using active-learning
techniques. These approaches provide strategies to choose the examples to label
before or during training. These strategies are usually based on heuristics or
even theoretical measures, but are not learned as they are directly used during
training. We design a model which aims at \textit{learning active-learning
strategies} using a meta-learning setting. More specifically, we consider a
pool-based setting, where the system observes all the examples of the dataset
of a problem and has to choose the subset of examples to label in a single
shot. Experiments show encouraging results
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Search-oriented conversational systems rely on information needs expressed in
natural language (NL). We focus here on the understanding of NL expressions for
building keyword-based queries. We propose a reinforcement-learning-driven
translation model framework able to 1) learn the translation from NL
expressions to queries in a supervised way, and, 2) to overcome the lack of
large-scale dataset by framing the translation model as a word selection
approach and injecting relevance feedback in the learning process. Experiments
are carried out on two TREC datasets and outline the effectiveness of our
approach.Comment: This is the author's pre-print version of the work. It is posted here
for your personal use, not for redistribution. Please cite the definitive
version which will be published in Proceedings of the 2018 EMNLP Workshop
SCAI: The 2nd International Workshop on Search-Oriented Conversational AI -
ISBN: 978-1-948087-75-
Multi-View Data Generation Without View Supervision
The development of high-dimensional generative models has recently gained a
great surge of interest with the introduction of variational auto-encoders and
generative adversarial neural networks. Different variants have been proposed
where the underlying latent space is structured, for example, based on
attributes describing the data to generate. We focus on a particular problem
where one aims at generating samples corresponding to a number of objects under
various views. We assume that the distribution of the data is driven by two
independent latent factors: the content, which represents the intrinsic
features of an object, and the view, which stands for the settings of a
particular observation of that object. Therefore, we propose a generative model
and a conditional variant built on such a disentangled latent space. This
approach allows us to generate realistic samples corresponding to various
objects in a high variety of views. Unlike many multi-view approaches, our
model doesn't need any supervision on the views but only on the content.
Compared to other conditional generation approaches that are mostly based on
binary or categorical attributes, we make no such assumption about the factors
of variations. Our model can be used on problems with a huge, potentially
infinite, number of categories. We experiment it on four image datasets on
which we demonstrate the effectiveness of the model and its ability to
generalize.Comment: Published as a conference paper at ICLR 201
Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
This paper aims at the problem of link pattern prediction in collections of
objects connected by multiple relation types, where each type may play a
distinct role. While common link analysis models are limited to single-type
link prediction, we attempt here to capture the correlations among different
relation types and reveal the impact of various relation types on performance
quality. For that, we define the overall relations between object pairs as a
\textit{link pattern} which consists in interaction pattern and connection
structure in the network, and then use tensor formalization to jointly model
and predict the link patterns, which we refer to as \textit{Link Pattern
Prediction} (LPP) problem. To address the issue, we propose a Probabilistic
Latent Tensor Factorization (PLTF) model by introducing another latent factor
for multiple relation types and furnish the Hierarchical Bayesian treatment of
the proposed probabilistic model to avoid overfitting for solving the LPP
problem. To learn the proposed model we develop an efficient Markov Chain Monte
Carlo sampling method. Extensive experiments are conducted on several real
world datasets and demonstrate significant improvements over several existing
state-of-the-art methods.Comment: 19pages, 5 figure
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