copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
GitHub - locuslab SATNet: Bridging deep learning and logical reasoning . . . SATNet is a differentiable (smoothed) maximum satisfiability (MAXSAT) solver that can be integrated into the loop of larger deep learning systems This (approximate) solver is based upon a fast coordinate descent approach to solving the semidefinite program (SDP) associated with the MAXSAT problem
[2310. 02133] Learning Reliable Logical Rules with SATNet Our approach builds upon SATNet, a differentiable MaxSAT solver that learns the underlying rules from input-output examples Despite its efficacy, the learned weights in SATNet are not straightforwardly interpretable, failing to produce human-readable rules
Learning Reliable Logical Rules with SATNet - arXiv. org Our approach builds upon SATNet, a differentiable MaxSAT solver that learns the underlying rules from input-output examples Despite its efficacy, the learned weights in SATNet are not straightforwardly interpretable, failing to produce human-readable rules
SATNET - Wikipedia SATNET, also known as the Atlantic Packet Satellite Network, was an early satellite network that formed an initial segment of the Internet It was implemented by BBN Technologies under the direction of ARPA
Learning Reliable Logical Rules with SATNet - OpenReview Our approach builds upon SATNet, a differentiable MaxSAT solver that learns the underlying rules from input-output examples Despite its eficacy, the learned weights in SATNet are not straight-forwardly interpretable, failing to produce human-readable rules
Understanding SATNet: Constraint Learning and Symbol Grounding . . . The SATNet framework is a neural architecture designed to learn instances of combinatorial problems by learning the set of logical constraints associated with an instance of the maximum satisfiability problem