Claims › ext:d6c48d55b89fa782 › line of work
Its line of work
Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs.
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 9.5 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 |
|---|---|---|---|---|---|---|
| By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within th… | ✓ supported | yes | 0.71 | 0 | 9.5 | — |
| Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking ne… | ✓ supported | yes | 0.71 | 0 | 3.2 | By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within th… |
See its whole group in the network, where it can be filtered and sized.
Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| 1 step below | supported | By training a range of conventional and spiking classifiers, we show that leveraging spike timing information within these datasets is essential for good class…this claim takes its method from it, as the citing paper says · human literature · ext:a2cc7abd02eaa7ae | 0.71 |
| this claim | supported | Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs o…human literature · ext:d6c48d55b89fa782 | 0.71 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.