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Found 7 papers, 0 papers with code
Parallel Algorithms for Exact Enumeration of Deep Neural Network Activation Regions
Our work has three main contributions: (1) we present a novel algorithm framework and parallel algorithms for region enumeration; (2) we implement one of our algorithms on a variety of network architectures and experimentally show how the number of regions dictates runtime; and (3) we show, using our algorithm's output, how the dimension of a region's affine transformation impacts further partitioning of the region by deeper layers.
Syntax-semantics interface: an algebraic model
We extend our formulation of Merge and Minimalism in terms of Hopf algebras to an algebraic model of a syntactic-semantic interface.
Old and New Minimalism: a Hopf algebra comparison
In this paper we compare some old formulations of Minimalism, in particular Stabler's computational minimalism, and Chomsky's new formulation of Merge and Minimalism, from the point of view of their mathematical description in terms of Hopf algebras.
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis
We analyzed the distribution of compchains in three UD English treebanks, EWT, GUM and LinES, revealing that these treebanks are sparse with respect to sentences with predicate-argument structure that includes predicate-argument embedding.
On the Computational Power of RNNs
Recent neural network architectures such as the basic recurrent neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks.
Evaluating the Ability of LSTMs to Learn Context-Free Grammars
On the other hand, we also observe that the model does not generalize well.
