A Multi-Task Representation Learning Architecture for Enhanced Graph Classification
نویسندگان
چکیده
منابع مشابه
Enhanced representation and multi-task learning for image annotation
In this paper we evaluate biased random sampling as image representation for bag of words models in combination with between class information transfer via output kernel-based multi-task learning using the ImageCLEF PhotoAnnotation dataset. We apply the mutual information measure for measuring correlation between kernels and labels. Biased random sampling improves ranking performance of classif...
متن کاملText-Enhanced Representation Learning for Knowledge Graph
Learning the representations of a knowledge graph has attracted significant research interest in the field of intelligent Web. By regarding each relation as one translation from head entity to tail entity, translation-based methods including TransE, TransH and TransR are simple, effective and achieving the state-of-the-art performance. However, they still suffer the following issues: (i) low pe...
متن کاملGraph Representation Learning and Graph Classification
Many real-world problems are represented by using graphs. For example, given a graph of a chemical compound, we want do determine whether it causes a gene mutation or not. As another example, given a graph of a social network, we want to predict a potential friendship that does not exist but it is likely to appear soon. Many of these questions can be answered by using machine learning methods i...
متن کاملAction-Affect Classification and Morphing using Multi-Task Representation Learning
Most recent work focused on affect from facial expressions, and not as much on body. This work focuses on body affect analysis. Affect does not occur in isolation. Humans usually couple affect with an action in natural interactions; for example, a person could be talking and smiling. Recognizing body affect in sequences requires efficient algorithms to capture both the micro movements that diff...
متن کاملMulti-task Representation Learning for Demographic Prediction
Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2020
ISSN: 1662-453X
DOI: 10.3389/fnins.2019.01395