« SEMANTiCS conference is the leading European conference on Semantic Technologies and AI. Researchers, industry experts and business leaders can develop a thorough understanding of trends and application scenarios in the fields of Machine Learning, Data Science, Linked Data and Natural Language Processing. »
« This work is devoted to the study of applicability of modern methods of machine learning to the task of automatic classification of scientific articles and abstracts. For this purpose, the study of such models of machine learning as artificial neural networks, random forest, logistic regression, and support vector machine was…
« This article seeks to assess the impact of data-driven methods of machine translation (MT), not just on translators, but more broadly on industry and society. (…) »
« Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has…
« We are in a digital age with Big Data at the heart of our global online environment. Exploiting Big Data by manual means is virtually impossible. We therefore need to rely on innovative methods such as Machine Learning and AI to allow us to fully harness the value of Big…
« (…) We present a neural dictionary model that can be used to predict if a phrase is synonymous to a concept in a reference ontology. Our model, called the Neural Concept Recognizer (NCR), uses a convolutional neural network to encode input phrases and then rank medical concepts based on the…
« Le projet Tamis, ou Traitement Algorithmique des Métadonnées en Imagerie et Sémantique, est un programme ayant pour vocation d’enrichir les métadonnées et la description d’un ouvrage grâce à son contenu. Pour ce faire, il fait appel à des technologies poussées de machine learning et d’intelligence artificielle.…
« (…) The link for the online version of the book is https://rafalab.github.io/dsbook/
The R markdown code used to generate the book are available on GitHub. Note that the individual files are not self contained since we run the code included in…
« Public datasets fuel the machine learning research rocket (h/t Andrew Ng), but it’s still too difficult to simply get those datasets into your machine learning pipeline. Every researcher goes through the pain of writing one-off scripts to download and prepare every dataset they work with, which…
« Since its inception in 1985, AISTATS has been an interdisciplinary gathering of researchers at the intersection of artificial intelligence, machine learning, statistics, and related areas. »
