Claims › ext:4456a1d62da8e613 › line of work
Its line of work
With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy.
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.
● 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 12.0 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
| Claim | Status | Checkable | Credence | Use | Stakes | Rests on |
|---|---|---|---|---|---|---|
| This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wide… | ○ unchecked | yes | 0.55 | 0 | 12.0 | — |
| With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one… | ○ unchecked | yes | 0.55 | 0 | 11.2 | This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wide… |
See its whole group in the network, where it can be filtered and sized.
Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| 1 step below | unchecked | This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures.this claim takes its method from it, as the citing paper says · human literature · ext:223290431aea5e15 | 0.55 |
| this claim | unchecked | With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch ac…human literature · ext:4456a1d62da8e613 | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.