1,253 research outputs found
Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework
The relevance of a document has many facets, going beyond the usual topical one, which have to be considered to satisfy a user's information need. Multiple representations of documents, like user-given reviews or the actual document content, can give evidence towards certain facets of relevance. In this respect polyrepresentation of documents, where such evidence is combined, is a crucial concept to estimate the relevance of a document. In this paper, we discuss how a geometrical retrieval framework inspired by quantum mechanics can be extended to support polyrepresentation. We show by example how different representations of a document can be modelled in a Hilbert space, similar to physical systems known from quantum mechanics. We further illustrate how these representations are combined by means of the tensor product to support polyrepresentation, and discuss the case that representations of documents are not independent from a user point of view. Besides giving a principled framework for polyrepresentation, the potential of this approach is to capture and formalise the complex interdependent relationships that the different representations can have between each other
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such methods can produce misleading predictions when used to combine
specialized classifiers. This work explores a novel approach. Instead of
combining predictions from individual classifiers directly, it first decomposes
the predictions into sets of pairwise preferences, treating them as transition
channels between classes, and thereon constructs a continuous-time Markov
chain, and use the equilibrium distribution of this chain as the final
prediction. This way allows us to form a coherent picture over all specialized
predictions. On large public datasets, the proposed method obtains considerable
improvement compared to mainstream ensemble methods, especially when the
classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implicit
feedback in search engines, as it confounds the relevance signal. While it was
recently shown how counterfactual learning-to-rank (LTR) approaches
\cite{Joachims/etal/17a} can provably overcome presentation bias when
observation propensities are known, it remains to show how to effectively
estimate these propensities. In this paper, we propose the first method for
producing consistent propensity estimates without manual relevance judgments,
disruptive interventions, or restrictive relevance modeling assumptions. First,
we show how to harvest a specific type of intervention data from historic
feedback logs of multiple different ranking functions, and show that this data
is sufficient for consistent propensity estimation in the position-based model.
Second, we propose a new extremum estimator that makes effective use of this
data. In an empirical evaluation, we find that the new estimator provides
superior propensity estimates in two real-world systems -- Arxiv Full-text
Search and Google Drive Search. Beyond these two points, we find that the
method is robust to a wide range of settings in simulation studies
Assessment of 48 Stock markets using adaptive multifractal approach
Stock market comovements are examined using cointegration, Granger causality
tests and nonlinear approaches in context of mutual information and
correlations. Underlying data sets are affected by non-stationarities and
trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock
markets cointegrated, and according to the Granger causality and mutual
information, we realize that the strongest relations lies between emerging
markets, and between emerging and frontier markets. According to scaling
exponent given by AMF-DFA, , we find that all underlying data sets
belong to non-stationary process. According to EMH, only 8 markets are
classified in uncorrelated processes at confidence interval. 6 Stock
markets belong to anti-correlated class and dominant part of markets has memory
in corresponding daily index prices during January 1995 to February 2014.
New-Zealand with and Jordan with are far
from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost
multifractal for all pair of Stock markets. The empirical relation, , is confirmed. Mentioned relation for is also
satisfied while for there is a deviation from this relation confirming
behavior of markets for small fluctuations is affected by contribution of major
pair. For larger fluctuations, the cross-correlation contains information from
both local and global conditions. Width of singularity spectrum for
auto-correlation and cross-correlation are and , respectively. The
wide range of singularity spectrum for cross-correlation confirms that the
bilateral relation between Stock markets is more complex. The value of
indicates that all pairs of stock market studied in this time
interval belong to cross-correlated processes.Comment: 16 pages, 13 figures and 4 tables, major revision and match to
published versio
Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification
Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimerâs disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (âWhich change in voxels would change the outcome most?â), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (âWhy does this person have AD?â) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual âfingerprintsâ of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
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