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Computational semiotics is empirical.
Burton Lancaster
PRO
RiverRider
6
3
17
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ARUNAGIRINATHAN's profile picture
rtnsht's profile picture
hugginface82-1's profile picture
34 followers
·
36 following
https://sunstonenorth.com
Space-Bacon
AI & ML interests
Explainable AI
Recent Activity
replied
to
their
post
about 5 hours ago
A 339 KB linear probe on frozen features beats the fine-tuned baseline on ChestX-ray14. Linear(5376, 14) on frozen google/gemma-4-31B-it hidden states. No fine-tuning, no radiology pretraining, no augmentation. All 112,120 images, official test_list.txt. Wang et al. 2017, ResNet-50 fine-tuned end to end 0.7451 this probe, frozen backbone + linear head 0.7590 view-position only (shortcut baseline) 0.5896 shuffled labels (refit floor) 0.5002 Ahead on 12 of 14 findings. The comparison is split-matched, and that took care to get right. The number everyone quotes, CheXNet's 0.8414, is on a different test set: their own random 70/10/20 partition, not the official list. Do not compare 0.7590 to it. The matched row is from Wang's v5 appendix, added specifically to report the published split. I had this wrong in our own code for a day, quoting a cross-split reference as a head-to-head, which is the error worth not repeating in public. Three controls, because a bare AUROC here is not interpretable. Shuffled labels catch leakage. View-only catches the shortcut, since portable AP films are taken of sicker patients, and it is folded, because Hernia's raw view-only of 0.3436 is really 0.6564 of shortcut once flipped. Intervals resample patients and not images, since the test split is 25,596 films from 2,797 patients. Banked negatives are on the card too. Max-pooling and top-16 pooling were predicted to help focal findings and did the opposite, costing 0.0537 and 0.0225. Readout depth barely matters, 0.7600 to 0.7605. Scope: detection, not early detection. Research artifact, not a diagnostic device. The backbone never runs in the demo. What ships is the reading. Space: https://huggingface.co/spaces/RiverRider/srt-cxr14-probe Model: https://huggingface.co/RiverRider/srt-cxr14-linear-probe Data + states: https://huggingface.co/datasets/RiverRider/srt-cxr14-frozen-probe
commented
on
a paper
about 9 hours ago
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
published
an
article
about 12 hours ago
Frozen Backbones Read Each Other
View all activity
Organizations
RiverRider
's models
23
Sort: Recently updated
RiverRider/srt-cxr14-linear-probe
Image Classification
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Updated
about 15 hours ago
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1
RiverRider/srt-cxr14-pooled-probe
Image Classification
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Updated
about 15 hours ago
RiverRider/srt-omni-xvendor-towers
Feature Extraction
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Updated
2 days ago
RiverRider/srt-omni-shared-tower
Feature Extraction
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Updated
2 days ago
RiverRider/srt-nla-av-gemma4
Feature Extraction
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Updated
2 days ago
RiverRider/srt-nla-gemma4-artifacts
Feature Extraction
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Updated
2 days ago
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1
RiverRider/srt-sunstone-linear-head
Feature Extraction
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Updated
2 days ago
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1
RiverRider/srt-verbalizer-v1
Text Generation
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Updated
2 days ago
RiverRider/gemma-4-31B-it-nf4
Image-Text-to-Text
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31B
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Updated
2 days ago
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43
RiverRider/srt-browser-head-118k
Feature Extraction
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Updated
2 days ago
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1
RiverRider/Gemma-4-31B-it-SRT-Sunstone
Feature Extraction
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Updated
Jul 3
RiverRider/srt-adapter-gptoss20b
Feature Extraction
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Updated
Jul 2
RiverRider/srt-nla-av-gptoss20b
Feature Extraction
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Updated
Jul 2
RiverRider/srt-nla-av-gemma2-2b-v1
Feature Extraction
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Jun 18
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12
RiverRider/srt-adapter-qwen3-235b
Feature Extraction
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Updated
Jun 18
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15
RiverRider/srt-nla-av-llama32-3b
Feature Extraction
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Jun 18
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13
RiverRider/srt-nla-av-v1
Feature Extraction
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Jun 18
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15
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4
RiverRider/zooL4nD3r-v0.1
Feature Extraction
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Jun 18
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24
RiverRider/srt-adapter-v22c_a050
Feature Extraction
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Updated
Jun 18
RiverRider/srt-adapter-v1.0
Feature Extraction
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Updated
Jun 18
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41
RiverRider/srt-adapter-v21a
Feature Extraction
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Updated
Jun 18
RiverRider/srt-adapter-v18
Feature Extraction
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Jun 18
RiverRider/srt-adapter-v8a
Feature Extraction
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Jun 18
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