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Its line of work

Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.

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 ScienceWe show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k sin… takes its method from Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. (identified in the literature)We show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k sin…: unchecked, credence 0.55, stakes 10.2We show improvements…Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.: unchecked, credence 0.55, stakes 12.2, reliance 1.0Specifically, we show…

● 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 12.2 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 show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k sin…○ uncheckedyes0.55010.2Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.
Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.○ uncheckedyes0.55012.2—

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

Step by step

WhereStatusClaimCredence
this claimuncheckedSpecifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images.human literature · ext:e91eb70b651dad7f0.55
1 step aboveuncheckedWe show improvements on several image classification and object detection tasks, and report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4%…takes its method from this claim, as the citing paper says · human literature · ext:d2f63dc3bc6be50d0.55

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