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Found 8 papers, 6 papers with code
Safety without alignment
Currently, the dominant paradigm in AI safety is alignment with human values.
Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets
1) Dataflow matrix machines (DMMs) generalize neural nets by replacing streams of numbers with linear streams (streams supporting linear combinations), allowing arbitrary input and output arities for activation functions, countable-sized networks with finite dynamically changeable active part capable of unbounded growth, and a very expressive self-referential mechanism.
Dataflow Matrix Machines as a Model of Computations with Linear Streams
We overview dataflow matrix machines as a Turing complete generalization of recurrent neural networks and as a programming platform.
Notes on Pure Dataflow Matrix Machines: Programming with Self-referential Matrix Transformations
Dataflow matrix machines are self-referential generalized recurrent neural nets.
Programming Languages
Programming Patterns in Dataflow Matrix Machines and Generalized Recurrent Neural Nets
Dataflow matrix machines arise naturally in the context of synchronous dataflow programming with linear streams.
Dataflow matrix machines as programmable, dynamically expandable, self-referential generalized recurrent neural networks
Dataflow matrix machines are a powerful generalization of recurrent neural networks.
Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks
Dataflow matrix machines are a powerful generalization of recurrent neural networks.
Linear Models of Computation and Program Learning
We consider two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation.
