Neural Guided Constraint Logic Programming for Program Synthesis · HackerPapers discussions

Neural Guided Constraint Logic Programming for Program Synthesis — mentioned by eihli

Thu, 02 Jan 2025 15:09:53 GMT

Hey! Thanks for all of the work you do. I love reading your stuff. Lately, I've been playing a lot with the code from Neural Guided Constraint Logic Programming for Program Synthesis [1]. I want to apply it to searching for neural network architectures. Something like: (define-relation (layero layer in out) ;; [2] (conde ((== layer `(Linear ,in ,out))) ((== layer `(Relu ,in ,out)) (== in out)) ((fresh (percent) (== layer `(Dropout ,in ,out ,percent)) (== in out) (countero percent) (<o '(1) percent) (<o percent '(0 0 1 0 0 1 1)))))) And a `layerso` that is just a sequence of `layers`. And then…

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Neural Guided Constraint Logic Programming for Program Synthesis — mentioned by remilouf

Tue, 15 Aug 2023 12:13:50 GMT

IanCal said it all. But for alternative approaches that also use LLM (with miniKanren) you can check https://arxiv.org/abs/1809.02840

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Neural Guided Constraint Logic Programming for Program Synthesis — mentioned by abeppu

Tue, 20 Jun 2023 15:33:39 GMT

> I call it program synthesis. In ChatGPT you can get ChatGPT to generate code, so this kind of already exists. This is cool, but to be clear work on program synthesis predates ChatGPT by _decades_. For a long time the work was searching for a program which met some behavioral specification. Emphasis shifted later towards inductive "programming-by-example" where we synthesize a program based on a small set of inputs and outputs, in part because providing those examples is often easier than thinking through an exact "specification" of what you want. Some work from the '70s…

Paper and discussion history. A mention is not an endorsement.