1,655 research outputs found
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
In this paper we propose a general framework for learning distributed
representations of attributes: characteristics of text whose representations
can be jointly learned with word embeddings. Attributes can correspond to
document indicators (to learn sentence vectors), language indicators (to learn
distributed language representations), meta-data and side information (such as
the age, gender and industry of a blogger) or representations of authors. We
describe a third-order model where word context and attribute vectors interact
multiplicatively to predict the next word in a sequence. This leads to the
notion of conditional word similarity: how meanings of words change when
conditioned on different attributes. We perform several experimental tasks
including sentiment classification, cross-lingual document classification, and
blog authorship attribution. We also qualitatively evaluate conditional word
neighbours and attribute-conditioned text generation.Comment: 11 pages. An earlier version was accepted to the ICML-2014 Workshop
on Knowledge-Powered Deep Learning for Text Minin
Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine.
The electrical demand forecasting problem can be regarded as a non-linear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high resolution. To solve this challenging problem, various time series and machine learning approaches has been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by being stochastic and allowing bi-directional connections between neurons. In this paper, we investigate a newly developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for demand forecasting. The assessment is made on the EcoGrid EU dataset, consisting of aggregated electric power consumption, price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to real-time pricing signals. The results show that for the energy prediction problem solved here, FCRBM outperforms the benchmark machine learning approach, i.e. Support Vector Machine
Improving Maritime Traffic Emission Estimations on Missing Data with CRBMs
Maritime traffic emissions are a major concern to governments as they heavily
impact the Air Quality in coastal cities. Ships use the Automatic
Identification System (AIS) to continuously report position and speed among
other features, and therefore this data is suitable to be used to estimate
emissions, if it is combined with engine data. However, important ship features
are often inaccurate or missing. State-of-the-art complex systems, like CALIOPE
at the Barcelona Supercomputing Center, are used to model Air Quality. These
systems can benefit from AIS based emission models as they are very precise in
positioning the pollution. Unfortunately, these models are sensitive to missing
or corrupted data, and therefore they need data curation techniques to
significantly improve the estimation accuracy. In this work, we propose a
methodology for treating ship data using Conditional Restricted Boltzmann
Machines (CRBMs) plus machine learning methods to improve the quality of data
passed to emission models. Results show that we can improve the default methods
proposed to cover missing data. In our results, we observed that using our
method the models boosted their accuracy to detect otherwise undetectable
emissions. In particular, we used a real data-set of AIS data, provided by the
Spanish Port Authority, to estimate that thanks to our method, the model was
able to detect 45% of additional emissions, of additional emissions,
representing 152 tonnes of pollutants per week in Barcelona and propose new
features that may enhance emission modeling.Comment: 12 pages, 7 figures. Postprint accepted manuscript, find the full
version at Engineering Applications of Artificial Intelligence
(https://doi.org/10.1016/j.engappai.2020.103793
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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