Search Results for author: Takeshi Yamada

Found 4 papers, 2 papers with code

Permuton-induced Chinese Restaurant Process

1 code implementation NeurIPS 2021 Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda

Our main contribution is to introduce the notion of permutons into the well-known Chinese restaurant process (CRP) for sequence partitioning: a permuton is a probability measure on $[0, 1]\times [0, 1]$ and can be regarded as a geometric interpretation of the scaling limit of permutations.

Baxter Permutation Process

1 code implementation NeurIPS 2020 Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda

Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.

Bayesian Inference

Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions

no code implementations NeurIPS 2015 Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada

We propose a kernel-based method for finding matching between instances across different domains, such as multilingual documents and images with annotations.

Modeling Social Annotation Data with Content Relevance using a Topic Model

no code implementations NeurIPS 2009 Tomoharu Iwata, Takeshi Yamada, Naonori Ueda

We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services.

General Classification Information Retrieval +1

Cannot find the paper you are looking for? You can Submit a new open access paper.