Nettsider med emneord «machine learning»
Christos Dimitrakakis talks about the challenge of identity protection.
João Gama, Associate Professor at the University of Porto, talks about the limitations of current machine learning and data mining algorithms in dealing with real-time data.
Water flow on, in and under glaciers still remains a poorly understood system. Water can flow over the surface of glaciers, as well as through channels inside and under the ice. The water thereby has strong influence both on ice melt and glacier dynamics, making glacial hydrology an important field of study.
The use of lexical semantic information for the task of syntactic parsing has seen varied success. Recently, however, the use of lexical semantic clusters derived from large corpora has been shown to improve parsing performance. It is still unclear, however, how different properties of these clusters affect results. This project aims to investigate the use of different types of clusters during syntactic parsing.
More precisely the idea is to use word clusters as a source for features in a statistical disambiguation model for a dependency parser. Generally, the clusters will group together words with similar distributional properties. The exact nature of these similarity relations, however, will vary depending on the types of context features that are used when performing the clustering. For this project we will basically be doing an extrinsic form of cluster evaluation then; investigating how different clustering parameters in turn affect the performance of a statistical parser.
Oppgaven utføres i samarbeid med Forsvarets forskningsinstitutt (FFI) på Kjeller.
Classification models are often used to make decisions that affect humans: whether to approve a loan application, extend a job offer, or provide insurance. In such applications, it is desirable for the individuals to not only knowing the results but also have the ability to change the decisions of the model. For example, when a person is denied a loan by a credit scoring model, in addition to know why he/she can not received the loan, it is meaningful for the person to know what he/she can do to influence the decision, i.e. what are the input variables that, if values are changed, can alter the decision of the model. Otherwise, without this information, he/she will be denied the loan as long as the model is deployed, and – more importantly – will lack agency over a decision that affects their livelihood.
Ontologies are developed for representing requirements and specifications that can help domain experts and engineers do various tasks in the real world. Usually, as the ontologies are quite large, it takes a long time for human users to understand the ontologies and it is also quite difficult for machines to do reasoning on such large ontologies. The goal is that we can explore different machine-learning techniques to extract relevant parts of the ontology and reduce reasoning time.
The PLUMBIN' project is a cross-disciplinary collaboration between high-energy physics and statistics aimed at making powerful computational tools for exploring the fundamental constituents of the Universe.
Martin de los Rios, The ICTP South American Institute for Fundamental Research
Water flow on, in and under glaciers still remains a poorly understood system. Water can flow over the surface of glaciers, as well as through channels inside and under the ice. The water thereby has a strong influence both on ice melt and glacier dynamics, making glacial hydrology an important field of study.
Water flow on, in and under glaciers still remains a poorly understood system. Water can flow over the surface of glaciers, as well as through channels inside and under the ice. The water thereby has a strong influence both on ice melt and glacier dynamics, making glacial hydrology an important field of study.
Tuyen Trung Truong talks about the gradient descent method, and why such a simple method performs so well in practice.
