2018年3月のニューロコンピューティング研究会にて発表.確率行列分解のBayes汎化誤差に関する理論的な不等式について数値実験を試みたかったが,そもそもBayes推定をすることが困難な問題であった:パラメータが単体(simplex)上に存在するために,事後分布からサンプリングを行うことが難しい.そこで本研究ではハミルトニアンモンテカルロ法という効率的なMCMC法を用いてBayes推定をしてみた.理論値と比較し,確率行列分解に対するハミルトニアンモンテカルロ法の有効性を検証した.in Japanese
2018年3月のニューロコンピューティング研究会にて発表.確率行列分解のBayes汎化誤差に関する理論的な不等式について数値実験を試みたかったが,そもそもBayes推定をすることが困難な問題であった:パラメータが単体(simplex)上に存在するために,事後分布からサンプリングを行うことが難しい.そこで本研究ではハミルトニアンモンテカルロ法という効率的なMCMC法を用いてBayes推定をしてみた.理論値と比較し,確率行列分解に対するハミルトニアンモンテカルロ法の有効性を検証した.in Japanese
Predicting protein–protein interactions based only on sequences information
Juwen Shen, Jian Zhang, Xiaomin Luo, Weiliang Zhu, Kunqian Yu, Kaixian Chen, Yixue Li and Hualiang Jiang
Proc Natl Acad Sci USA, 2007, 104(11), 4337-4341.
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Paper reading - Dropout as a Bayesian Approximation: Representing Model Uncer...Akisato Kimura
Introducing the paper "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning" presented in ICML2016 (in Japanese).
Updated version of https://www.slideshare.net/akisatokimura/paper-reading-dropout-as-a-bayesian-approximation-representing-model-uncertainty-in-deep-learning
Paper reading - Dropout as a Bayesian Approximation: Representing Model Uncer...Akisato Kimura
A stale version, please check https://www.slideshare.net/akisatokimura/paper-reading-dropout-as-a-bayesian-approximation-representing-model-uncertainty-in-deep-learning-166237519 for a new version.
Introducing the paper "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning" presented in ICML2016 (in Japanese).
NIPS2015 reading - Learning visual biases from human imaginationAkisato Kimura
1) The document discusses a paper on improving visual recognition systems by leveraging human visual biases and generating images from random features.
2) It describes estimating visual biases from human psychophysics experiments, then using those biases to reconstruct images from random features. The reconstructed images can then be used to train machine learning models.
3) The document outlines experiments showing that incorporating estimated human visual biases into machine learning models, such as SVMs, can help improve visual recognition performance compared to models trained without biases.
CVPR2015 reading "Global refinement of random forest"Akisato Kimura
- A method is presented for refining a pre-trained random forest by optimizing the leaf weights while keeping the tree structures fixed.
- This reformulates the random forest as a linear classification/regression problem where samples are represented by sparse indicator vectors.
- The optimization can be performed efficiently and the refined forest has comparable or better accuracy than the original forest, but with significantly fewer trees/nodes.
- Experiments on classification and regression datasets demonstrate the proposed method outperforms other random forest techniques while accelerating training and testing.
Computational models of human visual attention driven by auditory cuesAkisato Kimura
This document summarizes a presentation on computational models of human visual attention driven by auditory cues. It discusses how auditory information can modulate visual attention by selecting visual features that are synchronized with detected auditory events. The proposed model uses Bayesian surprise to detect transient events in visual and auditory streams separately, then correlates the two to select synchronized visual features. An evaluation of the model on video clips found it outperformed baseline models at predicting eye movements.
Brief description of the paper "Large-scale visual sentiment ontology and detectors using adjective noun pairs" presented in ACM Multimedia 2013 as a full paper.
Briefly reviews International Conference on Weblogs and Social Media (ICWSM12) from my perspective.
The latter part written in Japanese, sorry for that.