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Found 9 papers, 5 papers with code
Autoencoding Conditional Neural Processes for Representation Learning
Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data.
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations.
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning.
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel.
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem.
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Word embedding techniques heavily rely on the abundance of training data for individual words.
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 to the crucial role that effective handling of infrequent words can play in accurate semantic understanding.
Learning Rare Word Representations using Semantic Bridging
We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as cross-lingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge.
