Pith Number

pith:SYEL732W

pith:2026:SYEL732W6KW6XCO5UDXL5WDWEF

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Unsupervised learning of acquisition variability in structural connectomes via hybrid latent space modeling

Adam M. Saunders, Aravind R. Krishnan, Bennett A. Landman, Chenyu Gao, Daniel C. Moyer, Derek Archer, Elyssa McMaster, Gaurav Rudravaram, Jongyeon Yoon, Karthik Ramadass, Laura A. Barquero, Laurie B. Cutting, Lianrui Zuo, Lori L. Beason Held, Micah DArchangel, Murat Bilgel, Nancy R. Newlin, Praitayini Kanakaraj, Timothy J. Hohman, Tin Q. Nguyen

Hybrid latent space modeling with architectural annealing separates acquisition variability from biological signals in structural connectomes.

arxiv:2605.13933 v1 · 2026-05-13 · cs.LG · cs.AI · stat.ML

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\pithnumber{SYEL732W6KW6XCO5UDXL5WDWEF}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our architectural annealing produces stronger site learning (ARI=0.53, p<0.05) than these baselines. Results show that a hybrid continuous-discrete latent space, with architectural rather than loss-based annealing, provides a useful unsupervised mechanism for capturing acquisition variability in dMRI.

C2weakest assumption

That the discrete latent component specifically isolates acquisition variability rather than other unmeasured categorical factors such as age groups or disease status, and that the architectural annealing reliably prevents the discrete capacity from collapsing or absorbing continuous variance.

C3one line summary

A hybrid VAE with architectural annealing learns discrete clusters aligned with scanner and protocol differences in a dataset of 7416 structural connectomes spanning 13 studies.

References

46 extracted · 46 resolved · 1 Pith anchors

[1] 1986 , isbn = 1986

[2] Distilling the knowledge in a neural network , author=

[3] Advances in neural information processing systems , volume=

[4] Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , volume= 2009

[5] European journal of radiology , volume= 2011

Receipt and verification

First computed	2026-05-17T23:39:13.957363Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9608bfef56f2adeb89dda0eebed876216f071225838bbe3857d05ee6f46ee0e6

Aliases

arxiv: 2605.13933 · arxiv_version: 2605.13933v1 · doi: 10.48550/arxiv.2605.13933 · pith_short_12: SYEL732W6KW6 · pith_short_16: SYEL732W6KW6XCO5 · pith_short_8: SYEL732W

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SYEL732W6KW6XCO5UDXL5WDWEF \
  | 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: 9608bfef56f2adeb89dda0eebed876216f071225838bbe3857d05ee6f46ee0e6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "cb602efc973523abe655d073cd740241bf8ab4ed73e850f9cad44288dfa31c64",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T16:11:49Z",
    "title_canon_sha256": "fe8a048d96c788449a7d9662070466f628847ec186ae3efa9cae63bddaa31fc4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13933",
    "kind": "arxiv",
    "version": 1
  }
}