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Summary: Permits a MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the observed tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Summary: Permits a MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the observed tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #3145 +/- ##
=======================================
Coverage 99.97% 99.97%
=======================================
Files 219 219
Lines 21221 21241 +20
=======================================
+ Hits 21216 21236 +20
Misses 5 5 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Summary: Pull Request resolved: meta-pytorch#3145 Permits an MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the target tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Summary: Permits an MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the target tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Summary: Permits an MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the target tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Summary: Permits an MTGP to predict on an unobserved task, addressing these issues: meta-pytorch#2360 meta-pytorch#3085 To do this, we assume that the unobserved task is maximally correlated with the target tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption. Will come in handy for TL initialization. Differential Revision: D90769576
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Summary:
Permits an MTGP to predict on an unobserved task, addressing these issues:
#2360
#3085
To do this, we assume that the unobserved task is maximally correlated with the target tasks (equally with each, by averaging the elements). Exact heuristic on correlation is definitely up for discussion, but this seems like a decent default assumption.
Will come in handy for TL initialization.
Differential Revision: D90769576