A review of relational machine learning for knowledge graphs
Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …
graph-structured, data. In this paper, we provide a review of how such statistical models can …
Machine knowledge: Creation and curation of comprehensive knowledge bases
Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
relationships has been a longstanding goal of AI. Over the last decade, large-scale …
A survey on data collection for machine learning: a big data-ai integration perspective
Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …
multiple communities. There are largely two reasons data collection has recently become a …
Zero-shot recognition via semantic embeddings and knowledge graphs
We consider the problem of zero-shot recognition: learning a visual classifier for a category
with zero training examples, just using the word embedding of the category and its …
with zero training examples, just using the word embedding of the category and its …
[КНИГА][B] Machine learning for text: An introduction
CC Aggarwal, CC Aggarwal - 2018 - Springer
The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …
referred to as text mining, text analytics, or machine learning from text. The choice of …
Vqa: Visual question answering
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given
an image and a natural language question about the image, the task is to provide an …
an image and a natural language question about the image, the task is to provide an …
Knowledge vault: A web-scale approach to probabilistic knowledge fusion
Recent years have witnessed a proliferation of large-scale knowledge bases, including
Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase …
Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase …
Learning a deep convnet for multi-label classification with partial labels
Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …
ImageNet), but it is necessary to move beyond the single-label classification task because …
Learning to represent knowledge graphs with gaussian embedding
The representation of a knowledge graph (KG) in a latent space recently has attracted more
and more attention. To this end, some proposed models (eg, TransE) embed entities and …
and more attention. To this end, some proposed models (eg, TransE) embed entities and …
Distributed graphlab: A framework for machine learning in the cloud
While high-level data parallel frameworks, like MapReduce, simplify the design and
implementation of large-scale data processing systems, they do not naturally or efficiently …
implementation of large-scale data processing systems, they do not naturally or efficiently …