Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
×
Your browser does not support the video tag.
Skip to next slide
Ad
×
Share Slideshare
LinkedIn
Facebook
Twitter
Embed
Size (px)
Show related Slideshows at end
WordPress Shortcode
Link
Share
Email
2024 Trend Updates: What Really Wor...
by Search Engine Jou...
1062387 views
Storytelling For The Web: Integrate...
by Chiara Aliotta
980817 views
Artificial Intelligence, Data and C...
by OECD Directorate ...
921734 views
How to Leverage AI to Boost Employe...
by SocialHRCamp
391483 views
2024 State of Marketing Report – by...
by Marius Sescu
221384 views
Everything You Need To Know About C...
by Expeed Software
237443 views
View on Slideshare
1
of
33
Ad
View on Slideshare
1
of
33
Ad
![1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
"Meta-Learning Probablistic Inference for Prediction"
副題: Amortized...](https://image.slidesharecdn.com/20181214dl-181218052422/75/DL-Meta-Learning-Probabilistic-Inference-for-Prediction-1-2048.jpg?cb=1667684352)
![メタ学習とは？
• (http://ibisforest.org/index.php?メタ学習 より．2018/12/08)
• (おそらく[Vilalta and Drissi 2002]の翻訳)
ある決まったバイアス，すなわち仮説空間の中か...](https://image.slidesharecdn.com/20181214dl-181218052422/75/DL-Meta-Learning-Probabilistic-Inference-for-Prediction-5-2048.jpg?cb=1667684352)
![Metric-based Few-shot learning
• 𝑞 𝜙 𝜓 𝑡 𝐷 𝑡 , 𝜃 の設計:
• 𝑃 𝑦 𝑡 𝑥 𝑡, 𝜓 𝑡 , 𝜃 の設計:
• 該当する研究
• Prototypical Nets [Snell+2017]
...](https://image.slidesharecdn.com/20181214dl-181218052422/75/DL-Meta-Learning-Probabilistic-Inference-for-Prediction-19-2048.jpg?cb=1667684352)
![提案手法: Versatile 回帰タスク
• xは視点でyは画像を想定．設定はGQN[Eslami+ 2018]と似てる
• グローバルパラメータ𝜃はGenerator
• 訓練データセット 𝑥𝑖
𝑡
, 𝑦𝑖
𝑡
𝑖=1
𝑘
を入力に潜在変...](https://image.slidesharecdn.com/20181214dl-181218052422/75/DL-Meta-Learning-Probabilistic-Inference-for-Prediction-25-2048.jpg?cb=1667684352)
![• データセット:
• omniglot
• miniImageNet
• SOTA
• 注：近年few-shot界隈ではResNetを特徴
抽出に使うだけでSOTAな結果が得られ
ることが[Chen+2018]等で指摘されてい
るが，この論文...](https://image.slidesharecdn.com/20181214dl-181218052422/75/DL-Meta-Learning-Probabilistic-Inference-for-Prediction-28-2048.jpg?cb=1667684352)
