83 citations · 536 across the 64 of their papers we have counts for
73 papers
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…
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations
Matthias Lindemann, Alexander Koller, Ivan Titov
Seq2seq models have been shown to struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions of phenomena that the model handles corre…
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…
Fingerprint of vortex-like flux closure in isotropic Nd-Fe-B bulk magnet
Mathias Bersweiler, Yojiro Oba, Evelyn Pratami Sinaga +5
Taking advantage of recent progress in neutron instrumentation and in the understanding of magnetic-field-dependent small-angle neutron scattering, here, we study the three-dimensi…
Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality
Verna Dankers, Ivan Titov
A recent line of work in NLP focuses on the (dis)ability of models to generalise compositionally for artificial languages. However, when considering natural language tasks, the dat…
Hierarchical Phrase-based Sequence-to-Sequence Learning
Bailin Wang, Ivan Titov, Jacob Andreas +1
We describe a neural transducer that maintains the flexibility of standard sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases as a source of inductive b…