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井上研究室
Showing the original Japanese · View translation
情報を確率のモデルで読み解き、知能をつくる土台を築く。
AboutAbout the lab
知能をつくる、その土台から。
井上研究室は、
人間並みの
情報を
この
Key terms
- 高次元の
分布 - たくさんの
変数が 同時に どんな 値を とりやすいかの 散らばり方。 変数が 増える ほど 扱いが 難しくなる - グラフィカルモデル
- 多くの
変数の あいだの 関係を、 点と 線で つないだ図の 形に 表した 確率の モデル - 圧縮センシング
- 事前の
知識を 手が かりに、 少ない 観測データからもとの 信号や 画像を 求める 方法
NewsLab news
View More ↗ResearchResearch themes
最終的に
*Illustrative imageグラフィカルモデルの構造推定
工業機械に
*Illustrative image深度センサーによる物体の追跡
物体は
*Illustrative imageMRI画像の再構成
MRIは
*Illustrative imageCT画像の再構成
X線CT検査は
MembersFaculty
井上研究室の
- Professor井上 真郷
VoicesStudent voices
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The information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Tabuleiros de Galton (antes e depois)” Exhibit made by Estes Objethos Atelier, photo by Rodrigo Tetsuo Argenton / CC BY-SA 4.0 (cropped)
- グラフィカルモデルの
構造推定: “Spider web Luc Viatour” Luc Viatour / CC BY-SA 3.0 (cropped) - 深度センサーに
よる物体の 追跡: “Austin Texas LiDAR Point Cloud (Highway 183)” Rebecca equator / CC BY-SA 4.0 (cropped) - MRI画像の
再構成: “MRI brain sagittal section” everyone's idle / CC BY-SA 2.0 (cropped) - CT画像の
再構成: “CT Scanner (NIH BioArt )” NIAID Ryan Kissinger / Public domain (cropped) - Related lab (村田研究室): “BalticServers data center” BalticServers.com / CC BY-SA 3.0 (cropped)
- Related lab (天沢研究室): “Traditional Japanese wrapping cloth,huroshiki,katori-city,japan” katorisi / CC BY 3.0 (cropped)
- Related lab (岩崎研究室): “Cyanobacteria 029” Lamiot / CC BY-SA 3.0 (cropped)
- Related lab (大久保研究室): “Lithium-Ion Cell cylindric” RudolfSimon / CC BY-SA 3.0 (cropped)
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