#擴散模型
Live, measured metrics for the hashtag #擴散模型 from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #擴散模型
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.
Day-by-day usage
measured · mastodon.online (Mastodon public tags API) · fetched 2026-08-23 01:03 UTC1 uses by 1 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.online (Mastodon public search API) · fetched 2026-08-23 01:03 UTCNo related tags with measured usage found for #擴散模型.
Live pulse
measured · mastodon.online (Mastodon tag timeline) · fetched 2026-08-23 01:03 UTCEverything below is measured over the latest 22 public posts (spanning ~25987 hours).
Posting hours (UTC) — busiest: 19:00
Languages: Chinese (Taiwan) (21) · Chinese (1)
Avg boosts / post: 0
Top of the latest posts
🌘 《DiffusionGemma 技術報告》 ➤ 擺脫逐字解碼瓶頸，離散擴散技術如何賦予 Gemma 4 極致推論速度 ✤ https://arxiv.org/abs/2608.00146 本報告推出實驗性開源權重語言模型 DiffusionGemma，該模型採用離散擴散技術，突破傳統自迴歸模型逐字解碼的瓶頸，能以平行方式反覆修改由 256 個 Token 組成的區塊。研發團隊並非從頭訓練，而是微調擁有 252 億參數（其中 38 億為動態激活）的混合專家模型 Gemma
🌗 學習擴散模型的積分：流映射（Flow Maps）詳解 ➤ 從繁瑣的迭代去噪，邁向直接的路徑預測 ✤ https://sander.ai/2026/05/06/flow-maps.html 生成式 AI 正面臨一個關鍵難題：擴散模型（Diffusion Models）的採樣過程過於繁瑣且緩慢。傳統做法需要透過無數次微小的迭代，像走迷宮一樣一點點去噪，最終將雜訊轉化為數據。Sander Dieleman 在本文中深入淺出地介紹了「流映射」（Flow Maps）技術，這是一種
🌘 透過非梯度向量流進行流映射學習 ➤ 突破擴散模型採樣瓶頸：SGFlow 如何高效學習流映射 ✤ https://openreview.net/pdf?id=C1bkDPqvDW 本文介紹了一種名為 SGFlow 的創新方法，旨在解決擴散與流模型中生成樣本時高昂的計算開銷問題。傳統的一致性模型雖然透過直接學習 ODE 軌跡上的流映射來加速採樣，但往往伴隨著模型反轉困難、需要穿透嵌套模型進行反向傳播等技術痛點。SGFlow 巧妙地避開了這些限制，透過非保守動力學訓練模型，使
#擴散模型 across platforms
every network with a public tag surfaceFollow #擴散模型 straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
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/擴散模型