Hiroshi Sawada

Signal Processing · 2003–2016

A researcher in the Science corpus: 10 papers; trusts 88 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 37 citations into it; #1,427 of 7,572 by trust here, #1,485 by citations
• Artificial Intelligence — 33 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Optimization & Training — 13 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Computational Mechanics — 12 citations into it; #2,917 of 10,001 by trust here, #3,763 by citations
• Representation & Self-Supervised Learning — 11 citations into it; #2,269 of 3,145 by trust here, #3,009 by citations
• Information Systems — 9 citations into it

Whom Hiroshi Sawada trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Radford M. Neal
2. Anthony J. Bell
3. Shun-ichi Amari
4. Noboru Murata
5. Kiyotoshi Matsuoka
6. Özgür Yilmaz
7. Terrence J. Sejnowski
8. David M. Blei
9. Arthur P. Dempster
10. Bernhard Schölkopf

Who trusts Hiroshi Sawada overall

1. Soo-Young Lee
2. Seungjin Choi
3. Zhuo Chen
4. Takuya Yoshioka
5. Sharon Gannot
6. Marc Delcroix
7. Bracha Laufer-Goldshtein
8. Zbyněk Koldovský
9. Jinyu Li
10. Kateřina Žmolíková

Papers from Hiroshi Sawada's perspective

Ordered by how strongly the researchers Hiroshi Sawada trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Fashion Coordinates Recommender System Using Photographs from Fashion Magazines (2011) — 125 citations
• Efficient K-Nearest Neighbor Graph Construction Using MapReduce for Large-Scale Data Sets (2014) — 11 citations
• Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015) — 12 citations
• Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization (2016) — 336 citations
• Latent Support Measure Machines for Bag-of-Words Data Classification (2014) — 32 citations
• Fast approximate similarity search based on degree-reduced neighborhood graphs (2011) — 44 citations
• Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (2011) — 370 citations
• Blind Speech Separation (2007) — 400 citations
• A robust and precise method for solving the permutation problem of frequency-domain blind source separation (2004) — 621 citations
• Direction of arrival estimation for multiple source signals using independent component analysis (2003) — 74 citations