Download free for 30 days
Sign in
Upload
Language (EN)
Support
Business
Mobile
Social Media
Marketing
Technology
Art & Photos
Career
Design
Education
Presentations & Public Speaking
Government & Nonprofit
Healthcare
Internet
Law
Leadership & Management
Automotive
Engineering
Software
Recruiting & HR
Retail
Sales
Services
Science
Small Business & Entrepreneurship
Food
Environment
Economy & Finance
Data & Analytics
Investor Relations
Sports
Spiritual
News & Politics
Travel
Self Improvement
Real Estate
Entertainment & Humor
Health & Medicine
Devices & Hardware
Lifestyle
Change Language
Language
English
Español
Português
Français
Deutsche
Cancel
Save
Submit search
EN
Uploaded by
ohken
11,252 views
最適輸送の計算アルゴリズムの研究動向
東大数理有志研究会(takamatsu26)、機械学習勉強会(Math-iine Learning)での発表資料.
Science
◦
Read more
6
Save
Share
Embed
Embed presentation
Download
Downloaded 16 times
1
/ 29
2
/ 29
3
/ 29
4
/ 29
5
/ 29
6
/ 29
7
/ 29
8
/ 29
9
/ 29
10
/ 29
11
/ 29
12
/ 29
13
/ 29
14
/ 29
15
/ 29
16
/ 29
Most read
17
/ 29
18
/ 29
19
/ 29
Most read
20
/ 29
21
/ 29
22
/ 29
23
/ 29
Most read
24
/ 29
25
/ 29
26
/ 29
27
/ 29
28
/ 29
29
/ 29
More Related Content
PDF
最適輸送入門
by
joisino
PDF
最適輸送の解き方
by
joisino
PPTX
[DL輪読会]相互情報量最大化による表現学習
by
Deep Learning JP
PDF
変分推論と Normalizing Flow
by
Akihiro Nitta
PPTX
[DL輪読会]ドメイン転移と不変表現に関するサーベイ
by
Deep Learning JP
PPTX
[DL輪読会]Neural Ordinary Differential Equations
by
Deep Learning JP
PDF
[DL輪読会]Wasserstein GAN/Towards Principled Methods for Training Generative Adv...
by
Deep Learning JP
PDF
Learning with a Wasserstein Loss (NIPS2015)
by
Hayato Watanabe
最適輸送入門
by
joisino
最適輸送の解き方
by
joisino
[DL輪読会]相互情報量最大化による表現学習
by
Deep Learning JP
変分推論と Normalizing Flow
by
Akihiro Nitta
[DL輪読会]ドメイン転移と不変表現に関するサーベイ
by
Deep Learning JP
[DL輪読会]Neural Ordinary Differential Equations
by
Deep Learning JP
[DL輪読会]Wasserstein GAN/Towards Principled Methods for Training Generative Adv...
by
Deep Learning JP
Learning with a Wasserstein Loss (NIPS2015)
by
Hayato Watanabe
What's hot
PPTX
【DL輪読会】Scaling Laws for Neural Language Models
by
Deep Learning JP
PPTX
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
by
Yusuke Uchida
PDF
21世紀の手法対決 (MIC vs HSIC)
by
Toru Imai
PDF
SSII2022 [TS1] Transformerの最前線〜 畳込みニューラルネットワークの先へ 〜
by
SSII
PPTX
勾配ブースティングの基礎と最新の動向 (MIRU2020 Tutorial)
by
RyuichiKanoh
PDF
PRML学習者から入る深層生成モデル入門
by
tmtm otm
PDF
最新リリース：Optuna V3の全て - 2022/12/10 Optuna Meetup #2
by
Preferred Networks
PDF
変分推論法（変分ベイズ法）(PRML第10章)
by
Takao Yamanaka
PDF
データに内在する構造をみるための埋め込み手法
by
Tatsuya Shirakawa
PDF
異常検知と変化検知 9章 部分空間法による変化点検知
by
hagino 3000
PDF
[DL輪読会]Model soups: averaging weights of multiple fine-tuned models improves ...
by
Deep Learning JP
PDF
Disentanglement Survey:Can You Explain How Much Are Generative models Disenta...
by
Hideki Tsunashima
PPTX
【DL輪読会】SimCSE: Simple Contrastive Learning of Sentence Embeddings (EMNLP 2021)
by
Deep Learning JP
PDF
SSII2022 [SS2] 少ないデータやラベルを効率的に活用する機械学習技術 〜 足りない情報をどのように補うか？〜
by
SSII
PDF
ELBO型VAEのダメなところ
by
KCS Keio Computer Society
PPTX
Triplet Loss 徹底解説
by
tancoro
PDF
因果探索: 基本から最近の発展までを概説
by
Shiga University, RIKEN
PPTX
[DL輪読会]Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
by
Deep Learning JP
PDF
【メタサーベイ】数式ドリブン教師あり学習
by
cvpaper. challenge
PDF
機械学習で泣かないためのコード設計
by
Takahiro Kubo
【DL輪読会】Scaling Laws for Neural Language Models
by
Deep Learning JP
Swin Transformer (ICCV'21 Best Paper) を完璧に理解する資料
by
Yusuke Uchida
21世紀の手法対決 (MIC vs HSIC)
by
Toru Imai
SSII2022 [TS1] Transformerの最前線〜 畳込みニューラルネットワークの先へ 〜
by
SSII
勾配ブースティングの基礎と最新の動向 (MIRU2020 Tutorial)
by
RyuichiKanoh
PRML学習者から入る深層生成モデル入門
by
tmtm otm
最新リリース：Optuna V3の全て - 2022/12/10 Optuna Meetup #2
by
Preferred Networks
変分推論法（変分ベイズ法）(PRML第10章)
by
Takao Yamanaka
データに内在する構造をみるための埋め込み手法
by
Tatsuya Shirakawa
異常検知と変化検知 9章 部分空間法による変化点検知
by
hagino 3000
[DL輪読会]Model soups: averaging weights of multiple fine-tuned models improves ...
by
Deep Learning JP
Disentanglement Survey:Can You Explain How Much Are Generative models Disenta...
by
Hideki Tsunashima
【DL輪読会】SimCSE: Simple Contrastive Learning of Sentence Embeddings (EMNLP 2021)
by
Deep Learning JP
SSII2022 [SS2] 少ないデータやラベルを効率的に活用する機械学習技術 〜 足りない情報をどのように補うか？〜
by
SSII
ELBO型VAEのダメなところ
by
KCS Keio Computer Society
Triplet Loss 徹底解説
by
tancoro
因果探索: 基本から最近の発展までを概説
by
Shiga University, RIKEN
[DL輪読会]Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
by
Deep Learning JP
【メタサーベイ】数式ドリブン教師あり学習
by
cvpaper. challenge
機械学習で泣かないためのコード設計
by
Takahiro Kubo
More from ohken
PDF
ICML 2020 最適輸送まとめ
by
ohken
PPTX
Sliced Wasserstein距離と生成モデル
by
ohken
PDF
ICLR2020読み会 Stable Rank Normalization
by
ohken
PPTX
Qiskit Advocate 自己紹介
by
ohken
PDF
Associative Memory Model について
by
ohken
PDF
Multivariate Time series analysis via interpretable RNNs
by
ohken
ICML 2020 最適輸送まとめ
by
ohken
Sliced Wasserstein距離と生成モデル
by
ohken
ICLR2020読み会 Stable Rank Normalization
by
ohken
Qiskit Advocate 自己紹介
by
ohken
Associative Memory Model について
by
ohken
Multivariate Time series analysis via interpretable RNNs
by
ohken
Download
![[DL輪読会]相互情報量最大化による表現学習](https://cdn.slidesharecdn.com/ss_thumbnails/20190913iwasawa-190913002312-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DL輪読会]ドメイン転移と不変表現に関するサーベイ](https://cdn.slidesharecdn.com/ss_thumbnails/20190614iwasawa-190614005939-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DL輪読会]Neural Ordinary Differential Equations](https://cdn.slidesharecdn.com/ss_thumbnails/nnasode1-190111001755-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DL輪読会]Wasserstein GAN/Towards Principled Methods for Training Generative Adv...](https://cdn.slidesharecdn.com/ss_thumbnails/wgan-1-170224021826-thumbnail.jpg?width=640&height=640&fit=bounds)
![SSII2022 [TS1] Transformerの最前線〜 畳込みニューラルネットワークの先へ 〜](https://cdn.slidesharecdn.com/ss_thumbnails/ts120220608ssiitransformerr2-220607054025-3adacf07-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DL輪読会]Model soups: averaging weights of multiple fine-tuned models improves ...](https://cdn.slidesharecdn.com/ss_thumbnails/dl0401-220405031053-thumbnail.jpg?width=640&height=640&fit=bounds)
![SSII2022 [SS2] 少ないデータやラベルを効率的に活用する機械学習技術 〜 足りない情報をどのように補うか？〜](https://cdn.slidesharecdn.com/ss_thumbnails/ss2ssii2022-220607054716-2760bd30-thumbnail.jpg?width=640&height=640&fit=bounds)
![[DL輪読会]Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets](https://cdn.slidesharecdn.com/ss_thumbnails/20220325okimura-220405024717-thumbnail.jpg?width=640&height=640&fit=bounds)
