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Found 9 papers, 4 papers with code
A logical word embedding for learning grammar
We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text.
A transparent approach to data representation
We use a binary attribute representation (BAR) model to describe a data set of Netflix viewers' ratings of movies.
Learning grammar with a divide-and-concur neural network
We implement a divide-and-concur iterative projection approach to context-free grammar inference.
Avoiding Traps in Nonconvex Problems
Iterative projection methods may become trapped at non-solutions when the constraint sets are nonconvex.
Reconstructing cellular automata rules from observations at nonconsecutive times
Recent experiments by Springer and Kenyon have shown that a deep neural network can be trained to predict the action of $t$ steps of Conway's Game of Life automaton given millions of examples of this action on random initial states.
Learning Without Loss
The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.).
Monotone Learning with Rectified Wire Networks
This constraint is simply that the value of the output node associated with the correct class should be zero.
A network that learns Strassen multiplication
We study neural networks whose only non-linear components are multipliers, to test a new training rule in a context where the precise representation of data is paramount.
An Improved Three-Weight Message-Passing Algorithm
We describe how the powerful "Divide and Concur" algorithm for constraint satisfaction can be derived as a special case of a message-passing version of the Alternating Direction Method of Multipliers (ADMM) algorithm for convex optimization, and introduce an improved message-passing algorithm based on ADMM/DC by introducing three distinct weights for messages, with "certain" and "no opinion" weights, as well as the standard weight used in ADMM/DC.
