research
∙
05/29/2023
Autoencoding Conditional Neural Processes for Representation Learning
Conditional neural processes (CNPs) are a flexible and efficient family ...
share
research
∙
05/09/2023
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
This work explores the utility of explicit structure for representation ...
research
∙
06/07/2021
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
To highlight the challenges of achieving representation disentanglement ...
research
∙
09/25/2020
Hierarchical Sparse Variational Autoencoder for Text Encoding
In this paper we focus on unsupervised representation learning and propo...
research
∙
09/30/2019
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Variational Autoencoders (VAEs) are known to suffer from learning uninfo...
research
∙
04/05/2019
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
We present a novel method for mapping unrestricted text to knowledge gra...
research
∙
11/12/2018
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Word embedding techniques heavily rely on the abundance of training data...
research
∙
08/28/2018
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models
Rare word representation has recently enjoyed a surge of interest, owing...
research
∙
07/24/2017
