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Found 20 papers, 3 papers with code
Asymptotically Optimal Regularization in Smooth Parametric Models
Many types of regularization schemes have been employed in statistical learning, each one motivated by some assumption about the problem domain.
Variational bounds for mixed-data factor analysis
We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods.
Group-sparse Embeddings in Collective Matrix Factorization
CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities.
Overlapping Trace Norms in Multi-View Learning
Multi-view learning leverages correlations between different sources of data to make predictions in one view based on observations in another view.
Approximate Inference with the Variational Holder Bound
We introduce the Variational Holder (VH) bound as an alternative to Variational Bayes (VB) for approximate Bayesian inference.
Online Learning to Sample
Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning.
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Embedding-based Knowledge Base Completion models have so far mostly combined distributed representations of individual entities or relations to compute truth scores of missing links.
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
Ranked #4 on
Link Prediction
on FB122
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
In this paper, we introduce the task of targeted aspect-based sentiment analysis.
Ranked #5 on
Aspect-Based Sentiment Analysis (ABSA)
on Sentihood
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Knowledge Graph Completion via Complex Tensor Factorization
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
Ranked #2 on
Knowledge Graphs
on FB15k
On Inductive Abilities of Latent Factor Models for Relational Learning
Latent factor models are increasingly popular for modeling multi-relational knowledge graphs.
Interpretation of Natural Language Rules in Conversational Machine Reading
This task requires both the interpretation of rules and the application of background knowledge.
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
