no code implementations • ICLR 2022 • Seiya Tokui, Issei Sato
We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors.
no code implementations • 1 Aug 2019 • Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent
Software frameworks for neural networks play a key role in the development and application of deep learning methods.
1 code implementation • 31 Mar 2018 • Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
no code implementations • ICML 2017 • Seiya Tokui, Issei Sato
The framework gives a natural derivation of the optimal estimator that can be interpreted as a special case of the likelihood-ratio method so that we can evaluate the optimal degree of practical techniques with it.
2 code implementations • ICML 2017 • Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on Unsupervised Image Classification on SVHN (using extra training data)
no code implementations • 4 Nov 2016 • Seiya Tokui, Issei Sato
Low-variance gradient estimation is crucial for learning directed graphical models parameterized by neural networks, where the reparameterization trick is widely used for those with continuous variables.
1 code implementation • NIPS 2015 • Seiya Tokui, Kenta Oono, Shohei Hido, Justin Clayton
Software frameworks for neural networks play key roles in the development and application of deep learning methods.