11,157 research outputs found
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
With widespread adoption of electronic health records, there is an increased
emphasis for predictive models that can effectively deal with clinical
time-series data. Powered by Recurrent Neural Network (RNN) architectures with
Long Short-Term Memory (LSTM) units, deep neural networks have achieved
state-of-the-art results in several clinical prediction tasks. Despite the
success of RNNs, its sequential nature prohibits parallelized computing, thus
making it inefficient particularly when processing long sequences. Recently,
architectures which are based solely on attention mechanisms have shown
remarkable success in transduction tasks in NLP, while being computationally
superior. In this paper, for the first time, we utilize attention models for
clinical time-series modeling, thereby dispensing recurrence entirely. We
develop the \textit{SAnD} (Simply Attend and Diagnose) architecture, which
employs a masked, self-attention mechanism, and uses positional encoding and
dense interpolation strategies for incorporating temporal order. Furthermore,
we develop a multi-task variant of \textit{SAnD} to jointly infer models with
multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we
demonstrate that the proposed approach achieves state-of-the-art performance in
all tasks, outperforming LSTM models and classical baselines with
hand-engineered features.Comment: AAAI 201
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Multitask learning without label correspondences
We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available. This is in contrast with existing methods which either assume that the label sets shared by different tasks are the same or that there exists a label mapping oracle. Our method directly maximizes the mutual information among the labels, and we show that the resulting objective function can be efficiently optimized using existing algorithms. Our proposed approach has a direct application for data integration with different label spaces for the purpose of classification, such as integrating Yahoo! and DMOZ web directories
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free
setting for a complete treatment of localized data corruptions due to severe
noise sources, e.g., an occluder in the case of a visual recording. Within this
framework, we propose i) a novel algorithm to efficiently separate, i.e.,
detect and localize, possible corruptions from a given suspicious data instance
and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As
a generalization to Euclidean distance, we also propose a novel distance
measure, which is based on the ranked deviations among the data attributes and
empirically shown to be superior in separating the corruptions. Our algorithm
first splits the suspicious instance into parts through a binary partitioning
tree in the space of data attributes and iteratively tests those parts to
detect local anomalies using the nominal statistics extracted from an
uncorrupted (clean) reference data set. Once each part is labeled as anomalous
vs normal, the corresponding binary patterns over this tree that characterize
corruptions are identified and the affected attributes are imputed. Under a
certain conditional independency structure assumed for the binary patterns, we
analytically show that the false alarm rate of the introduced algorithm in
detecting the corruptions is independent of the data and can be directly set
without any parameter tuning. The proposed framework is tested over several
well-known machine learning data sets with synthetically generated corruptions;
and experimentally shown to produce remarkable improvements in terms of
classification purposes with strong corruption separation capabilities. Our
experiments also indicate that the proposed algorithms outperform the typical
approaches and are robust to varying training phase conditions
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
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