50,580 research outputs found
Web Data Knowledge Extraction
A constantly growing amount of information is available through the web.
Unfortunately, extracting useful content from this massive amount of data still
remains an open issue. The lack of standard data models and structures forces
developers to create adhoc solutions from the scratch. The figure of the expert
is still needed in many situations where developers do not have the correct
background knowledge. This forces developers to spend time acquiring the needed
background from the expert. In other directions, there are promising solutions
employing machine learning techniques. However, increasing accuracy requires an
increase in system complexity that cannot be endured in many projects. In this
work, we approach the web knowledge extraction problem using an expertcentric
methodology. This methodology defines a set of configurable, extendible and
independent components that permit the reutilisation of large pieces of code
among projects. Our methodology differs from similar solutions in its
expert-driven design. This design, makes it possible for subject-matter expert
to drive the knowledge extraction for a given set of documents. Additionally,
we propose the utilization of machine assisted solutions that guide the expert
during this process. To demonstrate the capabilities of our methodology, we
present a real use case scenario in which public procurement data is extracted
from the web-based repositories of several public institutions across Europe.
We provide insightful details about the challenges we had to deal with in this
use case and additional discussions about how to apply our methodology
Unsupervised Deep Tracking
We propose an unsupervised visual tracking method in this paper. Different
from existing approaches using extensive annotated data for supervised
learning, our CNN model is trained on large-scale unlabeled videos in an
unsupervised manner. Our motivation is that a robust tracker should be
effective in both the forward and backward predictions (i.e., the tracker can
forward localize the target object in successive frames and backtrace to its
initial position in the first frame). We build our framework on a Siamese
correlation filter network, which is trained using unlabeled raw videos.
Meanwhile, we propose a multiple-frame validation method and a cost-sensitive
loss to facilitate unsupervised learning. Without bells and whistles, the
proposed unsupervised tracker achieves the baseline accuracy of fully
supervised trackers, which require complete and accurate labels during
training. Furthermore, unsupervised framework exhibits a potential in
leveraging unlabeled or weakly labeled data to further improve the tracking
accuracy.Comment: to appear in CVPR 201
Obligation and Prohibition Extraction Using Hierarchical RNNs
We consider the task of detecting contractual obligations and prohibitions.
We show that a self-attention mechanism improves the performance of a BILSTM
classifier, the previous state of the art for this task, by allowing it to
focus on indicative tokens. We also introduce a hierarchical BILSTM, which
converts each sentence to an embedding, and processes the sentence embeddings
to classify each sentence. Apart from being faster to train, the hierarchical
BILSTM outperforms the flat one, even when the latter considers surrounding
sentences, because the hierarchical model has a broader discourse view.Comment: 6 pages, short paper at ACL 201
Deep Self-taught Learning for Remote Sensing Image Classification
This paper addresses the land cover classification task for remote sensing
images by deep self-taught learning. Our self-taught learning approach learns
suitable feature representations of the input data using sparse representation
and undercomplete dictionary learning. We propose a deep learning framework
which extracts representations in multiple layers and use the output of the
deepest layer as input to a classification algorithm. We evaluate our approach
using a multispectral Landsat 5 TM image of a study area in the North of Novo
Progresso (South America) and the Zurich Summer Data Set provided by the
University of Zurich. Experiments indicate that features learned by a deep
self-taught learning framework can be used for classification and improve the
results compared to classification results using the original feature
representation.Comment: This is a corrected version of the final paper published in the
proceeding
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks
We study anomaly detection and introduce an algorithm that processes variable
length, irregularly sampled sequences or sequences with missing values. Our
algorithm is fully unsupervised, however, can be readily extended to supervised
or semisupervised cases when the anomaly labels are present as remarked
throughout the paper. Our approach uses the Long Short Term Memory (LSTM)
networks in order to extract temporal features and find the most relevant
feature vectors for anomaly detection. We incorporate the sampling time
information to our model by modulating the standard LSTM model with time
modulation gates. After obtaining the most relevant features from the LSTM, we
label the sequences using a Support Vector Data Descriptor (SVDD) model. We
introduce a loss function and then jointly optimize the feature extraction and
sequence processing mechanisms in an end-to-end manner. Through this joint
optimization, the LSTM extracts the most relevant features for anomaly
detection later to be used in the SVDD, hence completely removes the need for
feature selection by expert knowledge. Furthermore, we provide a training
algorithm for the online setup, where we optimize our model parameters with
individual sequences as the new data arrives. Finally, on real-life datasets,
we show that our model significantly outperforms the standard approaches thanks
to its combination of LSTM with SVDD and joint optimization.Comment: 11 page
Improved EEG Event Classification Using Differential Energy
Feature extraction for automatic classification of EEG signals typically
relies on time frequency representations of the signal. Techniques such as
cepstral-based filter banks or wavelets are popular analysis techniques in many
signal processing applications including EEG classification. In this paper, we
present a comparison of a variety of approaches to estimating and
postprocessing features. To further aid in discrimination of periodic signals
from aperiodic signals, we add a differential energy term. We evaluate our
approaches on the TUH EEG Corpus, which is the largest publicly available EEG
corpus and an exceedingly challenging task due to the clinical nature of the
data. We demonstrate that a variant of a standard filter bank-based approach,
coupled with first and second derivatives, provides a substantial reduction in
the overall error rate. The combination of differential energy and derivatives
produces a 24% absolute reduction in the error rate and improves our ability to
discriminate between signal events and background noise. This relatively simple
approach proves to be comparable to other popular feature extraction approaches
such as wavelets, but is much more computationally efficient.Comment: Published in IEEE Signal Processing in Medicine and Biology
Symposium. Philadelphia, Pennsylvania, US
A Data Ecosystem to Support Machine Learning in Materials Science
Facilitating the application of machine learning to materials science
problems will require enhancing the data ecosystem to enable discovery and
collection of data from many sources, automated dissemination of new data
across the ecosystem, and the connecting of data with materials-specific
machine learning models. Here, we present two projects, the Materials Data
Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address
these needs. We use examples to show how MDF and DLHub capabilities can be
leveraged to link data with machine learning models and how users can access
those capabilities through web and programmatic interfaces.Comment: 23 pages, 6 figures, submitted to MRS Communications special issue on
AI in Materials Scienc
Automatic Bridge Bidding Using Deep Reinforcement Learning
Bridge is among the zero-sum games for which artificial intelligence has not
yet outperformed expert human players. The main difficulty lies in the bidding
phase of bridge, which requires cooperative decision making under partial
information. Existing artificial intelligence systems for bridge bidding rely
on and are thus restricted by human-designed bidding systems or features. In
this work, we propose a pioneering bridge bidding system without the aid of
human domain knowledge. The system is based on a novel deep reinforcement
learning model, which extracts sophisticated features and learns to bid
automatically based on raw card data. The model includes an
upper-confidence-bound algorithm and additional techniques to achieve a balance
between exploration and exploitation. Our experiments validate the promising
performance of our proposed model. In particular, the model advances from
having no knowledge about bidding to achieving superior performance when
compared with a champion-winning computer bridge program that implements a
human-designed bidding system.Comment: 8 pages, 1 figure, 2016 ECAI accepte
A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
Legislation can be viewed as a body of prescriptive rules expressed in
natural language. The application of legislation to facts of a case we refer to
as statutory reasoning, where those facts are also expressed in natural
language. Computational statutory reasoning is distinct from most existing work
in machine reading, in that much of the information needed for deciding a case
is declared exactly once (a law), while the information needed in much of
machine reading tends to be learned through distributional language statistics.
To investigate the performance of natural language understanding approaches on
statutory reasoning, we introduce a dataset, together with a legal-domain text
corpus. Straightforward application of machine reading models exhibits low
out-of-the-box performance on our questions, whether or not they have been
fine-tuned to the legal domain. We contrast this with a hand-constructed
Prolog-based system, designed to fully solve the task. These experiments
support a discussion of the challenges facing statutory reasoning moving
forward, which we argue is an interesting real-world task that can motivate the
development of models able to utilize prescriptive rules specified in natural
language
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that must
be analyzed and interpreted by teams of physicists. This analysis is often
exploratory, where scientists are unable to enumerate the possible types of
signal prior to performing the experiment. Thus, tools for summarizing,
clustering, visualizing and classifying high-dimensional data are essential. In
this work, we show that meaningful physical content can be revealed by
transforming the raw data into a learned high-level representation using deep
neural networks, with measurements taken at the Daya Bay Neutrino Experiment as
a case study. We further show how convolutional deep neural networks can
provide an effective classification filter with greater than 97% accuracy
across different classes of physics events, significantly better than other
machine learning approaches
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