#SelfSupervisedLearning

Live, measured metrics for the hashtag #SelfSupervisedLearning from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.

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Own #selfsupervisedlearning

This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.

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card via hashtag.org · tokens via hashtag.space
0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
18
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 18
0.2
Avg reactions / post
Mastodon · last 18

Day-by-day usage

measured · mas.to (Mastodon public tags API) · fetched 2026-07-27 07:45 UTC
0
07-21
0
07-22
0
07-23
0
07-24
0
07-25
0
07-26
0
07-27

0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.

Related hashtags

measured · mas.to (Mastodon public search API) · fetched 2026-07-27 07:45 UTC

No related tags with measured usage found for #selfsupervisedlearning.

Live pulse

measured · mas.to (Mastodon tag timeline) · fetched 2026-07-27 07:45 UTC

Everything below is measured over the latest 18 public posts (spanning ~30677 hours).

Posting hours (UTC) — busiest: 07:00

00:0012:0023:00

Languages: English (15) · Italian (2) · French (1)

Avg boosts / post: 0.7

Top of the latest posts

  • From Julián Tachella @JulianTachella, posted on "Chi": ""Learning to reconstruct signals from binary measurements alone" We present theory and a #selfsupervised approach for learning to reconstruct incomplete and binary (!) measurements usi

    Low Rank Jack@[email protected]312023-07-24 21:20 UTCView post →
  • LLM Self Defense: By Self Examination LLMs Know They Are Being Tricked https://arxiv.org/abs/2308.07308 * LLM can generate harmful content in response to user prompts * even aligned language models are susceptible to adversarial attacks tha

    Victoria Stuart 🇨🇦 🏳️‍⚧️@[email protected]122023-08-17 16:42 UTCView post →
  • 🧠 New preprint by Fabian A. Mikulasch & @fzenke: Understanding Self-Supervised #Learning via #LatentDistribution Matching proposes a unifying theoretical framework for #SelfSupervisedLearning. The paper reframes #SSL as latent distribution

    Fabrizio Musacchio@[email protected]002026-05-07 14:50 UTCView post →

Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/selfsupervisedlearning