#SequenceModeling

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

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0
Uses / 7 days
Mastodon
0
Accounts / 7 days
Mastodon
6
Recent posts
Mastodon
~0/hr
Recent pace
Mastodon · last 6
0
Avg reactions / post
Mastodon · last 6

Day-by-day usage

measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-27 05:32 UTC
0
07-21
0
07-22
0
07-23
0
07-24
0
07-25
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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 · fosstodon.org (Mastodon public search API) · fetched 2026-07-27 05:32 UTC

No related tags with measured usage found for #sequencemodeling.

Live pulse

measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-27 05:32 UTC

Everything below is measured over the latest 6 public posts (spanning ~3606 hours).

Posting hours (UTC)

00:0012:0023:00

Languages: English (6)

Avg boosts / post: 0.2

Top of the latest posts

  • fly51fly (@fly51fly) Princeton University 연구진이 시간적 중간 계층 순환(Temporal Middle-Layer Recurrence)을 결합한 Transformer 구조인 T²MLR를 제안했습니다. Transformer 중간 레이어에 recurrence를 도입해 시계열·순차 데이터의 시간적 상태를 더 효율적으로 다루려는 연구로 보이며, 장기 컨텍스트 처리 및 순차 추론 구조를 탐색하는 개발자에

    ainews@[email protected]♥ 0↻ 12026-07-17 23:55 UTCView post →

  • fly51fly (@fly51fly) Linear RNN의 상태를 sparsity로 확장하는 Sparse Delta Memory 연구입니다. 희소성을 활용해 메모리 효율과 확장성을 개선하려는 접근으로 보이며, 장기 시퀀스 처리나 효율적인 RNN 계열 모델 설계에 관심 있는 개발자에게 참고할 만한 논문입니다. https://x.com/fly51fly/status/2075333365541360083 #rnn #sparsity #m

    ainews@[email protected]♥ 0↻ 02026-07-10 19:45 UTCView post →

  • fly51fly (@fly51fly) 순환 신경망을 명시적 recurrence 없이 사전학습하는 방법을 제안합니다. RNN 구조와 학습 방식에 대한 연구로, 시퀀스 모델 설계 관점에서 흥미로운 초기 연구이며 LLM/에이전트 개발에 바로 적용되는 수준은 아닙니다. https://x.com/fly51fly/status/2069172571619418431 #rnn #pretraining #sequencemodeling #resear

    ainews@[email protected]♥ 0↻ 02026-06-23 11:53 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/sequencemodeling