11 lines
2.5 KiB
JSON
11 lines
2.5 KiB
JSON
{
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"title": "Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation",
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"byline": null,
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"dir": null,
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"excerpt": "What follows is a high-level overview of this work, for more details refer to our paper. Given a reward \n \n \n \n R\n \n \n (\n \n \n x\n \n \n )\n \n \n \n R(x)\n \n and a deterministic episodic environment where episodes end with a ``generate \n \n \n \n x\n \n \n \n x\n \n '' action, how do we generate diverse and high-reward \n \n \n \n x\n \n \n \n x\n \n s?\n We propose to use Flow Networks to model discrete \n \n \n \n p\n \n \n (\n \n \n x\n \n \n )\n \n \n ∝\n \n \n R\n \n \n (\n \n \n x\n \n \n )\n \n \n \n p(x) \\propto R(x)\n \n from which we can sample sequentially (like episodic RL, rather than iteratively as MCMC methods would). We show that our method, GFlowNet, is very useful on a combinatorial domain, drug molecule synthesis, because unlike RL methods it generates diverse \n \n \n \n x\n \n \n \n x\n \n s by design.",
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"siteName": "fakehost",
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"siteIcon": "",
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"publishedDate": null,
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"readerable": true
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}
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