Accurate multilingual transfer parsing typically relies on careful feature engineering. In this
paper, we propose a hierarchical tensor-based approach for this task. This approach …

Weighted finite automata (WFA) are finite automata whose transitions and states are
augmented with some weights, elements of a semiring. A WFA induces a function over …

Motivation: Sequence discovery tools play a central role in several fields of computational
biology. In the framework of Transcription Factor binding studies, most of the existing motif …

Various forms of regularization in learning tasks strive for different notions of simplicity. This
paper presents a spectral regularization technique, which attaches a unique inductive bias …

This paper studies the following approximate minimization problem: given a minimal
weighted tree automaton A with n states recognizing a weighted tree language f, can we …

We present a low-rank multi-linear model for the task of solving prepositional phrase
attachment ambiguity (PP task). Our model exploits tensor products of word embeddings …

We propose a fast and scalable method for semi-supervised learning of sequence models,
based on anchor words and moment matching. Our method can handle hidden Markov …

We compare a classical CNN architecture for sequence classification involving several
convolutional and max-pooling layers against a simple model based on weighted finite state …

Spectral models for learning weighted non-deterministic automata have nice theoretical and
algorithmic properties. Despite this, it has been challenging to obtain competitive results in …

Spectral methods of moments provide a powerful tool for learning the parameters of latent
variable models. Despite their theoretical appeal, the applicability of these methods to real …