4 citations · 6 across the 8 of their papers we have counts for
9 papers
Autoencoding Conditional Neural Processes for Representation Learning
Victor Prokhorov, Ivan Titov, N. Siddharth
Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data. They have seen particular application in conte…
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
Mattia Opper, Victor Prokhorov, N. Siddharth
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 repr…
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
Lan Zhang, Victor Prokhorov, Ehsan Shareghi
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 app…
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
Victor Prokhorov, Yingzhen Li, Ehsan Shareghi +1
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…
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Victor Prokhorov, Ehsan Shareghi, Yingzhen Li +2
Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entangl…
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
Victor Prokhorov, Mohammad Taher Pilehvar, Nigel Collier
We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of…
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis +2
Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of…
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models
Mohammad Taher Pilehvar, Dimitri Kartsaklis, Victor Prokhorov +1
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
Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis +2
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 map…