47 citations · 262 across the 33 of their papers we have counts for
6 papers · 1 filter
RNNs Implicitly Implement Tensor Product Representations
R. Thomas McCoy, Tal Linzen, Ewan Dunbar +1
Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regu…
Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations
Matthias Lalisse, Paul Smolensky
Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate…
A Simple Recurrent Unit with Reduced Tensor Product Representations
Shuai Tang, Paul Smolensky, Virginia R. de Sa
idely used recurrent units, including Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structure…
Predicting the Argumenthood of English Prepositional Phrases
Najoung Kim, Kyle Rawlins, Benjamin Van Durme +1
Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such a…
Learning and analyzing vector encoding of symbolic representations
Roland Fernandez, Asli Celikyilmaz, Rishabh Singh +1
We present a formal language with expressions denoting general symbol structures and queries which access information in those structures. A sequence-to-sequence network processing…
Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation
Paul Tupper, Paul Smolensky, Pyeong Whan Cho
Gradient Symbolic Computation is proposed as a means of solving discrete global optimization problems using a neurally plausible continuous stochastic dynamical system. Gradient sy…