83 citations · 536 across the 64 of their papers we have counts for
5 papers · 1 filter
Theoretical and Practical Perspectives on what Influence Functions Do
Andrea Schioppa, Katja Filippova, Ivan Titov +1
Influence functions (IF) have been seen as a technique for explaining model predictions through the lens of the training data. Their utility is assumed to be in identifying trainin…
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…
Preventing Posterior Collapse with Levenshtein Variational Autoencoder
Serhii Havrylov, Ivan Titov
Variational autoencoders (VAEs) are a standard framework for inducing latent variable models that have been shown effective in learning text representations as well as in text gene…
Few-Shot Learning for Opinion Summarization
Arthur Bražinskas, Mirella Lapata, Ivan Titov
Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practicall…
Learning Semantic Script Knowledge with Event Embeddings
Ashutosh Modi, Ivan Titov
Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in muc…