深層学習理論チーム 鈴木大慈 Deep Learning Theory Team ......Input layer (SO 4 feature...

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深層学習理論チーム 鈴木大慈 Deep Learning Theory Team Taiji Suzuki Taiji Suzuki: Fast generalization error bound of deep learning from a kernel perspective. AISTATS2018. サイズが約三分の一でも精度向上提案手法 精度向上させつつ処理速度4倍

Transcript of 深層学習理論チーム 鈴木大慈 Deep Learning Theory Team ......Input layer (SO 4 feature...

Page 1: 深層学習理論チーム 鈴木大慈 Deep Learning Theory Team ......Input layer (SO 4 feature maps convolution layer sub-sampling layer I convolution layer sub-sampling layer

深層学習理論チーム 鈴木大慈Deep Learning Theory Team

Taiji Suzuki

Taiji Suzuki: Fast generalization error bound of deep learning from a kernel perspective. AISTATS2018.

サイズが約三分の一

でも精度向上

提案手法

精度向上させつつ処理速度4倍

Sampling dan Preparasi Sampling

Sampling dan Preparasi Sampling

Convolution et filtrage des signaux

Convolution et filtrage des signaux

第四章 網路層通訊協定 Network Layer Protocols. Protocol Stack Physical Layer Network Layer Data Link Layer Transport Layer Application Layer.

第四章 網路層通訊協定 Network Layer Protocols. Protocol Stack Physical Layer Network Layer Data Link Layer Transport Layer Application Layer.

Computer Vision: Correlation, Convolution, and Gradient

Computer Vision: Correlation, Convolution, and Gradient

BAB III SAMPLING BERKELOMPOK DAN SAMPLING …repository.upi.edu/20436/6/S_MAT_1100475_Chapter3.pdfBAB III SAMPLING BERKELOMPOK DAN SAMPLING BERKELOMPOK ...

BAB III SAMPLING BERKELOMPOK DAN SAMPLING …repository.upi.edu/20436/6/S_MAT_1100475_Chapter3.pdfBAB III SAMPLING BERKELOMPOK DAN SAMPLING BERKELOMPOK ...

Convolution Neural Network CNN A tutorial KH Wong Convolution Neural Network CNN ver. 4.11a1.

Convolution Neural Network CNN A tutorial KH Wong Convolution Neural Network CNN ver. 4.11a1.

GEII - Ma3 - Représentations de Fourier et convolution

GEII - Ma3 - Représentations de Fourier et convolution

Topological convolution algebras - BGU

Topological convolution algebras - BGU

03 Convolution

03 Convolution

nonprobability sampling menggunakan purposive sampling ... file11. teknik sampling dalam penelitian ini yaitu nonprobability sampling dengan menggunakan purposive sampling. 12. Jelaskan

nonprobability sampling menggunakan purposive sampling ... file11. teknik sampling dalam penelitian ini yaitu nonprobability sampling dengan menggunakan purposive sampling. 12. Jelaskan

Thin Layer Effect:Dipping Layer Refractions

Thin Layer Effect:Dipping Layer Refractions

HGCF: Hyperbolic Graph Convolution Networks for ...

HGCF: Hyperbolic Graph Convolution Networks for ...

1. Convolution et matrices

1. Convolution et matrices

Bab 10 Sampling Audit dalam Pengujian Pengendalianstaffnew.uny.ac.id/upload/132309995/pendidikan/10.+Sampling+Audit... · Teknik Sampling Audit Sampling Atribut Sampling Variabel

Bab 10 Sampling Audit dalam Pengujian Pengendalianstaffnew.uny.ac.id/upload/132309995/pendidikan/10.+Sampling+Audit... · Teknik Sampling Audit Sampling Atribut Sampling Variabel

UWB, 802.11,Coding for Cross-Layer Design: Research Sampling · UWB, 802.11,Coding for Cross-Layer Design: Research Sampling Predrag Spasojevic WINLAB, Rutgers University IAB Spring,

UWB, 802.11,Coding for Cross-Layer Design: Research Sampling · UWB, 802.11,Coding for Cross-Layer Design: Research Sampling Predrag Spasojevic WINLAB, Rutgers University IAB Spring,

CouCoursrs : Tr: Traitementaitement dd images’images · Plan du chapitre 1. Convolution Numérique 2. Filtrage par convolution 3. Filtres non linéaires (autre que convolution)

CouCoursrs : Tr: Traitementaitement dd images’images · Plan du chapitre 1. Convolution Numérique 2. Filtrage par convolution 3. Filtres non linéaires (autre que convolution)

Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―

Active Convolution, Deformable Convolution ―形状・スケールを学習可能なConvolution―

Scène = image « parfaite » inconnue déformation (modélisée par un filtre linéaire bruit additif inconnu (convolution inconnue H(z)) (convolution inverse.

Scène = image « parfaite » inconnue déformation (modélisée par un filtre linéaire bruit additif inconnu (convolution inconnue H(z)) (convolution inverse.

Convolution 2

Convolution 2

Layer Network Dan Layer Transport

Layer Network Dan Layer Transport