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Its line of work

This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets.

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).
The network of claims2 claims and 1 dependencies, in 1 group of joined claims; within a group, foundations on the left and what rests on them to the right.2 claims, 1 step deep, Computer ScienceThis approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIF… takes its method from We find that standard vision models become stable to SGD noise in this way early in training. (identified in the literature)We find that standard vision models become stable to SGD noise in this way early in training.: unchecked, credence 0.55, stakes 7.6, reliance 1.0We find that standard…This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIF…: unchecked, credence 0.55, stakes 0.0This approach…

● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable

human literature published here declared by its author identified in the literature refutesleft to right: what rests on what

size: stakes, by area; the largest here 7.6 the claim it is drawn around

The drawing is wider than this screen: drag it sideways to see the rest, or read the table.

Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
We find that standard vision models become stable to SGD noise in this way early in training.○ uncheckedyes0.5507.6—
This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIF…○ uncheckedyes0.5500.0We find that standard vision models become stable to SGD noise in this way early in training.

See its whole group in the network, where it can be filtered and sized.

Step by step

WhereStatusClaimCredence
1 step belowuncheckedWe find that standard vision models become stable to SGD noise in this way early in training.this claim takes its method from it, as the citing paper says · human literature · ext:31ad886dbb8607ed0.55
this claimuncheckedThis approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets.human literature · ext:bac1c172f5e1fe430.55

Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.