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Valuation of Startups: A Machine Learning Perspective
We address the problem of startup valuation from a machine learning perspective with a focus on European startups. More precisely, we aim to infer...
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A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series
We address, in the context of time series, the problem of learning a summary causal graph from observations through a model with independent and...
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LTR-expand: query expansion model based on learning to rank association rules
Query Expansion (QE) is widely applied to improve the retrieval performance of ad-hoc search, using different techniques and several data sources to...
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Pairwise-Constrained Deep Document Clustering
While in standard clustering no side information is used, users might be interested in providing additional information to influence the clustering....
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Seed-Guided Deep Document Clustering
Different users may be interested in different clustering views underlying a given collection (e.g., topic and writing style in documents). Enabling...
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Improving Arabic information retrieval using word embedding similarities
Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of...
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Efficient Association Rules Selecting for Automatic Query Expansion
Query expansion approaches based on term correlation such as association rules (ARs) have proved significant improvement in the performance of the...
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Bilingual Lexicon Extraction from Comparable Corpora Based on Closed Concepts Mining
In this paper, we propose to complement the context vectors used in bilingual lexicon extraction from comparable corpora with concept vectors, that...
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Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs
We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time...
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Arabic Text Classification Based on Word and Document Embeddings
Recently, Word Embeddings have been introduced as a major breakthrough in Natural Language Processing (NLP) to learn viable representation of...
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A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp
Estimating the centroid of a set of time series under time warp is a major topic for many temporal data mining applications, as summarization a set...
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
BackgroundThis article provides an overview of the first BioASQchallenge, a competition on large-scale biomedical semantic indexing and question...
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On Binary Reduction of Large-Scale Multiclass Classification Problems
In the context of large-scale problems, traditional multiclass classification approaches have to deal with class imbalancement and complexity issues...
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Supervised Topic Classification for Modeling a Hierarchical Conference Structure
In this paper we investigate the problem of supervised latent modeling for extracting topic hierarchies from data. The supervised part is given in...
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Algorithmic Robustness for Semi-Supervised \((\epsilon , \gamma , \tau )\) -Good Metric Learning
Using the appropriate metric is crucial for the performance of most of machine learning algorithms. For this reason, a lot of effort has been put...
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Multilingual Documents Clustering Based on Closed Concepts Mining
The scarcity of bilingual and multilingual parallel corpora has prompted many researchers to accentuate the need for new methods to enhance the...
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Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data
Hyper-parameter tuning is a resource-intensive task when optimizing classification models. The commonly used k-fold cross validation can become...
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Evaluation measures for hierarchical classification: a unified view and novel approaches
Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification...
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Exploring the Space of IR Functions
In this paper we propose an approach to discover functions for IR ranking from a space of simple closed-form mathematical functions. In general, all...
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Predicting Information Diffusion in Social Networks Using Content and User’s Profiles
Predicting the diffusion of information on social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral...
