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原 聡 研究室
Showing the original Japanese · View translation
機械学習の信頼性と使いやすさに取り組む、アルゴリズムの研究室。
AboutAbout the lab
信頼できる機械学習を、アルゴリズムから考える。
原研究室は、
複雑な
こうした
熟練の
Key terms
- 説明可能AI
- 機械学習モデルが
出した 判断の 理由を、 使う 人が 理解できるように 説明させる 技術 - ブラックボックス
- モデルの
判断の 理由を、 使う 人が 理解できない 状態。 複雑な モデルを 実用する ときの 課題の 一つ - バイアス
- データに
内在する 差別的な 偏り。 例と して 男女差別が あり、 モデルが そのまま 学習してしまう ことが ある
NewsLab news
量子状態トモグラフィの
論文 （主著は 特任助教）が、 NeurIPS2026 に 採択 修士課程の
学生が、 MIRU2026 で 学生奨励賞を 受賞 教授が、
目黒会賞を 受賞 主成分分析と
スペクトルクラスタリングの 一貫性の 論文が、 AISTATS2026 に 採択
ResearchResearch themes
原研究室では、
*Illustrative imageAIの判断理由を説明する
複雑な
*Illustrative imageモデルの公平性
機械学習モデルは、
*Illustrative image機械学習の使いやすさ向上
良い
*Illustrative imageデータ分析の落とし穴の解消
分析ツールを
MembersFaculty
原研究室の
- Professor原 聡
- Project Assistant Professor加治佐 貴大
VoicesStudent voices
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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: “Client Project Hashcat Testing With an AMD Epyc Supermicro Nvidia GPU Server Using Proxmox (Lawrence Systems) 07” Lawrence Systems / CC BY 3.0 (cropped)
- AIの
判断理由を 説明する : “Decision Tree Anatomy” Lapollard / CC BY-SA 4.0 (cropped) - モデルの
公平性: “Balance scale IMGP9755” Nikodem Nijaki / CC BY-SA 3.0 (cropped) - 機械学習の
使いやすさ向上: “RingaScientist-4” Kerstin Göpfrich / CC BY-SA 4.0 (cropped) - データ分析の
落と し穴の 解消: “Data visualization process v1” Farcaster at English Wikipedia / CC BY-SA 3.0 (cropped) - Related lab (植野 真臣 研究室): “Bayesian network” Sachs K, Gifford D, Jaakkola T, Sorger P, Lauffenburger DA. / CC BY-SA 4.0 (cropped)
- Related lab (原田 慧 研究室): “Real-time bus tracking control room in Lebanon” UrusHyby / CC0 (cropped)
- Related lab (幸島 匡宏 研究室): “Machine Learning cookies” Syced / CC0 (cropped)
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