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Found 11 papers, 3 papers with code
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution.
Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules
Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study.
A study of semantic augmentation of word embeddings for extractive summarization
In this study we examine the effect of semantic augmentation approaches on extractive text summarization.
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome.
ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training
Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies.
CLARIN-EL Web-based Annotation Tool
This paper presents a new Web-based annotation tool, the {``}CLARIN-EL Web-based Annotation Tool{''}.
NOMAD: Linguistic Resources and Tools Aimed at Policy Formulation and Validation
The NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies.
Task-Driven Linguistic Analysis based on an Underspecified Features Representation
In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.
Evaluation of Online Dialogue Policy Learning Techniques
The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers.
