Search Results for author:
Found 13 papers, 6 papers with code
Translating Embeddings for Modeling Multi-relational Data
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
Ranked #5 on
Link Prediction
on FB122
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features.
MMKG: Multi-Modal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
The existing literature on knowledge graph completion mostly focuses on the link prediction task.
Towards a Spectrum of Graph Convolutional Networks
We present our ongoing work on understanding the limitations of graph convolutional networks (GCNs) as well as our work on generalizations of graph convolutions for representing more complex node attribute dependencies.
TransRev: Modeling Reviews as Translations from Users to Items
This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review.
Learning Graph Representations with Embedding Propagation
We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data.
Net2Vec: Deep Learning for the Network
We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network.
Irreflexive and Hierarchical Relations as Translations
We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs.
Learning Representations of Missing Data for Predicting Patient Outcomes
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches.
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data
The most successful prior approaches for modeling such time series are based on recurrent neural networks that learn to impute unobserved values and then treat the imputed values as observed.
