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state-space-models

topic2 events
papersTODAY 04:00 UTC

Attention Bridge Method Distills Transformers into Mamba Models with Less Data

A new arXiv paper proposes an "attention bridge" technique for converting pretrained Transformer models into Mamba-style state-space models. The approach aims to make the distillation process more data efficient, addressing the high compute cost of training competitive SSMs from scratch. The work targets the gap between the mature Transformer ecosystem and the less developed tooling around state-space architectures.

distillation/ssm/transformers/research
papersSEP 10 04:00 UTC

Online Learning of Scale Parameters in Score-Driven Filters

A new research paper addresses how to learn the gain, the scale parameter that multiplies the scaled log-likelihood score in score-driven filters, directly online. Rather than fixing this coefficient beforehand, the method treats each admissible gain as selecting a reachable next state given the current state and the realized scaled score. This allows the filter's update step to adapt during operation.

time-series/online-learning/filtering