pith:66ZBTXSN
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
Time-varying deep state space models learn switching dynamics via a dictionary of time-evolving basis functions and outperform time-invariant models on synthetic and audio tasks.
arxiv:2605.15311 v1 · 2026-05-14 · cs.LG · cs.SY · eess.SY
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\pithnumber{66ZBTXSNP3CVZD6QIN2YC4IM6J}
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Claims
The proposed time-varying model consistently outperforms its time-invariant counterparts while maintaining comparable computational complexity on both synthetic switching systems and a speech denoising task with real audio corrupted by switching dynamics noise.
That the time-varying dynamics present in the target sequences can be adequately represented and learned through a fixed dictionary of basis functions in which each basis evolves differently over time, without requiring additional mechanisms for detecting or modeling the switches explicitly.
A class of time-varying deep state-space model neural networks is proposed that learns dynamics via a dictionary of basis functions evolving differently over time, outperforming time-invariant versions on switching synthetic data and speech denoising.
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Receipt and verification
| First computed | 2026-05-20T00:00:52.033889Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f7b219de4d7ec55c8fd0437581710cf246837857d90d9e65e6e31220f55fef7c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/66ZBTXSNP3CVZD6QIN2YC4IM6J \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f7b219de4d7ec55c8fd0437581710cf246837857d90d9e65e6e31220f55fef7c
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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