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Claims › ext:1879577a7a42d66f › line of work

Its line of work

Furthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive results.

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 ScienceFurthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive… takes its method from MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de… (identified in the literature)Furthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive…: unchecked, credence 0.55, stakes 8.5Furthermore, the tiny…MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de…: unchecked, credence 0.55, stakes 15.6, reliance 4.0MobileNets are based…

● 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 15.6 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
Furthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive…○ uncheckedyes0.5508.5MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de…
MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de…○ uncheckedyes0.55015.6—

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

Step by step

WhereStatusClaimCredence
1 step belowuncheckedMobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks.this claim takes its method from it, as the citing paper says · human literature · ext:245914e3c3cc70ff0.55
this claimuncheckedFurthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive results.human literature · ext:1879577a7a42d66f0.55

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