2 research outputs found
Discrete Denoising Diffusion Approach to Integer Factorization
Integer factorization is a famous computational problem unknown whether being
solvable in the polynomial time. With the rise of deep neural networks, it is
interesting whether they can facilitate faster factorization. We present an
approach to factorization utilizing deep neural networks and discrete denoising
diffusion that works by iteratively correcting errors in a partially-correct
solution. To this end, we develop a new seq2seq neural network architecture,
employ relaxed categorical distribution and adapt the reverse diffusion process
to cope better with inaccuracies in the denoising step. The approach is able to
find factors for integers of up to 56 bits long. Our analysis indicates that
investment in training leads to an exponential decrease of sampling steps
required at inference to achieve a given success rate, thus counteracting an
exponential run-time increase depending on the bit-length.Comment: International Conference on Artificial Neural Networks ICANN 202
Goal-Aware Neural SAT Solver
Modern neural networks obtain information about the problem and calculate the
output solely from the input values. We argue that it is not always optimal,
and the network's performance can be significantly improved by augmenting it
with a query mechanism that allows the network at run time to make several
solution trials and get feedback on the loss value on each trial. To
demonstrate the capabilities of the query mechanism, we formulate an
unsupervised (not depending on labels) loss function for Boolean Satisfiability
Problem (SAT) and theoretically show that it allows the network to extract rich
information about the problem. We then propose a neural SAT solver with a query
mechanism called QuerySAT and show that it outperforms the neural baseline on a
wide range of SAT tasks