|European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning|
Special sessions are organized by renowned scientists in their respective fields. Papers submitted to these sessions are reviewed according to the same rules as any other submission. Authors who submit papers to one of these sessions are invited to mention it on the author submission form; submissions to the special sessions must follow the same format, instructions and deadlines as any other submission, and must be sent according to the same procedure.
The following special sessions will be organized at ESANN2018:
Deep Learning in Bioinformatics and Medicine
Deep learning (DL) has been harnessing the attention of the machine learning research community over the latter years. Much of its success roots on having made available models and technologies capable of achieving ground-breaking performances in a variety of traditional fields of application of machine learning, such as machine vision and natural language processing (NLP).
Medicine, genetics, biology and chemistry are among the research fields where machine learning models find most consolidated applications. Admittedly, some of the DL flagships, like NLP and image processing have their implications in Medicine, e.g., in extracting information from the text of patients’ records or in analyzing medical imagery to find anomalous patterns. However, deep learning methodologies have only recently started to be used to address relevant bioinformatics and cheminformatics challenges. Reasons for such a slowed-down permeation can be sought in the complexity of the deep learning models which might prove difficult to use in novel application fields by non-machine learning experts. Lack of interpretability and insight into the trained models might also have been a limiting factor.
Despite such few limitations, deep learning methodologies offer far more enabling aspects and technologies for developing impacting contributions in bioinformatics research. Between the most relevant are the ability to effectively and efficiently process complex, large scale and multi-modal data, e.g. collections of biomedical images and associated patient information, DNA sequences, molecular graphs. The modular design of deep architectures together with the potential for re-using parts of previously trained models on novel tasks is another potential success enabler for bioinformatics applications.
This special session is meant to attract researchers who develop, investigate, or apply deep learning methods on biomedical and chemistry data. We aim to bring together researchers working on the topic from both the deep learning and the bioinformatics communities.
Topics include, but are not restricted to:
Machine Learning and Data Analysis in Astroinformatics
The ever-growing amount of data which becomes available in many domains clearly requires the development of efficient methods for data mining and analysis. These challenges occur in a variety of areas including societal issues, business and fundamental scientific research.
Astronomy continuous to be at the forefront of this development: Modern observational techniques provide enormous amounts of data, which have to be processed efficiently. The development of methods for their reliable acquisition and analysis has immediate impact on other areas including commercial applications, data security, environmental monitoring etc.
This special session is meant to attract researchers who develop, investigate or apply methods of machine learning and data analysis in the context of astronomical data.
Potential topics include, but are not limited to
Many methods have been developed in machine learning (ML) for information visualisation (infovis). For example, PCA, MDS, t-SNE and improvements are standard tools to reduce the dimensionality of high dimensional datasets for visualisation purposes. However, multiple other means are regularly used in the field of infovis when tackling datasets with high dimensionality. Letting the user manipulate the visualisation is one of these means, either through selection, navigation or filtering. Introducing manipulation of the visualisation also integrates the user as a core aspect of a given system. In the context of machine learning, beyond the informational and exploratory use of infovis, users' feedback can for example be highly informational to drive the dimensionality reduction process.
This special session of the ESANN conference is a followup of the special session on "Information Visualisation and Machine Learning: Techniques, Validation and Integration" at ESANN 2016. It aims to gather researchers that integrate users in the core of ML methods for infovis. New algorithms and frameworks are welcome, as well as experimental use cases that bring new insight in the integration of interaction and user integration in ML for infovis. This special session aims to provide practitioners from both communities a common forum of discussion where issues at the crossroads of machine learning and information visualisation could be discussed.
Topics of interest include, but are not limited to:
Emerging trends in machine learning: beyond conventional methods and data
Recently, new promising theoretical results, techniques, and methodologies have attracted the attention of many researchers and have allowed to broaden the range of applications in which machine learning can be effectively applied in order to extract useful and actionable information from the huge amount of heterogeneous data produced everyday by an increasingly digital world.
Examples of these methods and problems are:
Manual labeling of sufficient training data for diverse application domains is a costly, laborious task and often prohibitive. Therefore, designing models that can leverage rich labeled data in one domain and be applicable to a different but related domain is highly desirable. In particular, domain adaptation or transfer learning algorithms seek to generalize a model trained in a source domain (training data) to a new target domain (test data). The most common underlying assumption of many machine learning models is that both training and test data exhibit the same distribution or the same feature domains. However, in many real life problems, there is a distributional, feature space and/or dimension mismatch between the two domains or the statistical properties of the data evolve in time.
Transferring and incorporating different sources of information such as learned feature extractors, knowledge of labeled and unlabeled instances, learned parameters among others from different domains into a unified model that can leverage all the available prior knowledge in order to achieve human level accuracy on a given new task is of great importance. In this context, depending on the availability of the labeled and unlabeled training data from (i) source domains, (ii) source and target domains, different scenarios related to supervised as well as semi-supervised domain adaptation can for instance be considered. In addition different modeling strategies ranging from shallow to deep models is of interest.
Therefore, the main objective of the session is to discuss the recent rise of new research questions and learning strategies for the domain adaptation and transfer learning problems using both shallow and deep models. The goal is to promote a fruitful exchange of ideas and methods between different communities, leading to a global advancement of the field.
The topics of interest include but not limited to:
The use of randomization in the design of Neural Networks (NNs) has become increasingly popular, mainly due to the ease of implementation, extreme efficiency of the training algorithms and the possibility of analyzing the NNs properties that are independent from learning. Randomization can enter NN design in various disguises, for example in the model construction and training (e.g. random setting of a subset of weights), or in its functionality and regularization algorithms (e.g. inclusion of random noise in activation layers, drop-out techniques, etc.). Under a broader perspective, the analysis of randomized models naturally extends to a general Machine Learning (ML) context (e.g. random projections). Moreover, Learning in Structured Domains and Deep Learning represent ML research areas for which this type of analysis is highly beneficial.
This session calls for contributions targeting novel theoretical and/or empirical studies on randomization in NNs, and it is proposed as an opportunity for discussing the advantages and limitations/shortcomings of the approach under an open and critical perspective.
The topics of interest for the session include, but are not limited, to the following:
For about the last decade, the Big Data paradigm that has dominated research in machine learning can be summarized as follows: "It’s not who has the best algorithm that wins. It’s who has the most data." However, most data sets are biased and the corresponding biases are often ignored in research. This in turn makes the learned models unreliable. Concretely, the most frequently appearing biases in data sets are class imbalance and sample selection bias.
Class imbalance refers to a data set having a substantially different amount of examples per class. Models trained on such data sets often tend to predict the majority class, e.g. when using inappropriate metrics such as the accuracy. Sample selection bias refers to the problem of training data and production data having different distributions. In many real-world applications, this is a common issue because we do not have complete control over the data gathering process. Models trained on such training data poorly generalize to the production data. For both class imbalance and selection bias, having more representative data will help rather than just having a lot of more data.
This special session will be an opportunity for both researchers and practitioners, to present and discuss their latest works on the impact of biases in data sets on models and how these can be reduced. Topics include, but are not limited, to:
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