mLSTM + dynamic eval

Reported on 2 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical2 results

Language ModellingonText8
Bit per Character (BPC)· 2017-09-21
1.19
best: 1.63 (td-LSTM (Zhang et al., 2016))
Dynamic Evaluation of Neural Sequence Models arXiv:1709.07432
Language ModellingonHutter Prize
Bit per Character (BPC)· 2017-09-21
1.08
best: 1.31 (RHN - depth 5 [zilly2016recurrent])
Dynamic Evaluation of Neural Sequence Models arXiv:1709.07432