no code implementations • 19 Aug 2024 • Eito Ikuta, Yohan Lee, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

Extracting geometry features from photographic images independently of surface texture and transferring them onto different materials remains a complex challenge.

no code implementations • 22 May 2024 • Ryoya Yamasaki, Toshiyuki Tanaka

Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation.

1 code implementation • 21 May 2024 • Ryoya Yamasaki, Toshiyuki Tanaka

For $K$-class OR tasks, threshold methods learn a one-dimensional transformation (1DT) of the explanatory variable so that 1DT values for observations of the explanatory variable preserve the order of label values $1,\ldots, K$ for corresponding observations of the target variable well, and then assign a label prediction to the learned 1DT through threshold labeling, namely, according to the rank of an interval to which the 1DT belongs among intervals on the real line separated by $(K-1)$ threshold parameters.

no code implementations • 4 Mar 2024 • Hiroki Yasumoto, Toshiyuki Tanaka

Approximation capability of reservoir systems whose reservoir is a recurrent neural network (RNN) is discussed.

no code implementations • 1 Mar 2024 • Utako Yamamoto, Hirohiko Imai, Kei Sano, Masayuki Ohzeki, Tetsuya Matsuda, Toshiyuki Tanaka

The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data.

no code implementations • 23 Feb 2024 • Ryoya Yamasaki, Toshiyuki Tanaka

Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to their convergent points via iterative blurring.

no code implementations • 26 May 2023 • Daiki Miyake, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

In image editing employing diffusion models, it is crucial to preserve the reconstruction quality of the original image while changing its style.

Ranked #6 on Text-based Image Editing on PIE-Bench

no code implementations • 15 May 2023 • Ryoya Yamasaki, Toshiyuki Tanaka

The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE).

no code implementations • 15 May 2023 • Ryoya Yamasaki, Toshiyuki Tanaka

For example, in binary classification, instead of the one-hot target $(1, 0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing level $\alpha\in(0, 1)$, which causes squeezing of values of the logit.

no code implementations • 20 Apr 2023 • Ryoya Yamasaki, Toshiyuki Tanaka

Kernel-based modal statistical methods include mode estimation, regression, and clustering.

no code implementations • 24 Jun 2022 • Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda

Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.

no code implementations • 30 Jan 2020 • Ryoya Yamasaki, Toshiyuki Tanaka

Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model.

no code implementations • NeurIPS 2019 • Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda

By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.

no code implementations • ICLR 2019 • Akihiro Iohara, Takahito Ogawa, Toshiyuki Tanaka

Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling.

no code implementations • 21 Sep 2018 • Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka, Takeshi Kurashima, Maya Okawa, Hiroyuki Toda

With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity.

no code implementations • NeurIPS 2013 • Ryosuke Matsushita, Toshiyuki Tanaka

We study the problem of reconstructing low-rank matrices from their noisy observations.

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