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๐š™๐š›๐š˜๐š๐š’๐š•๐šŽ ๐šŸ๐š’๐šŽ๐š ๐šœ ๐™ถ๐š’๐š๐™ท๐šž๐š‹ ๐š๐š˜๐š•๐š•๐š˜๐š ๐šŽ๐š›๐šœ โ€ข ๐šƒ๐š˜๐š๐šŠ๐š• ๐š‚๐š๐šŠ๐š›๐šœ

Programming Languages

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Learning Languages

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Aboud me

  • ๐ŸŒฑ Iโ€™m currently learning the following.

    • Mathematical Optimization
    • Machine Learning
    • Deep Learning (mainly computer vision)
    • Quantum Computer (Combinatorial optimization)
    • Industrial Application

  • ๐Ÿง‘โ€๐Ÿ’ป My research topic is about adversarial examples, anomaly detection, and time series forecasting.

Profile

Education

  • M.S. in Mathematics, Kyushu University(Apr.2020 - Mar.2022 in Japan)
  • B.S. in Mathematics, Kyushu University(Apr.2016 - Mar.2020 in Japan)

Publications

  • ๆคๆœจ ไธ€ไนŸ๏ผˆๆ˜Žๆ˜Ÿๅคง๏ผ‰๏ผŒไฝ่—ค ้–‹๏ผŒ้ˆดๆœจ ่ฃ•็œŸ๏ผŒ้ซ™็”ฐ ๆ‹“ๅฎŸ๏ผŒๅฎ…ๅณถ ๅฏ›่ฒด๏ผŒๅ € ้š†ไน‹๏ผˆใ‚ฝใƒ•ใƒˆใƒใƒณใ‚ฏ๏ผ‰, ๆ˜ ๅƒๆคœ็ดขใฎ็ฒพๅบฆๅ‘ไธŠๆ–ฝ็ญ–๏ผš็”ปๅƒ็”Ÿๆˆใƒขใƒ‡ใƒซใ‚’็”จใ„ใŸใ‚ฏใ‚จใƒชๆ‹กๅผต, https://www.tc-iaip.org/dia/2024/program.html#os1
  • Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa, Nozomi Hata, Katsuki Fujisawa, Issa Oe, Hiroki Ishikura, Toru Mitsutake, Diversified Adversarial Attacks based on Conjugate Gradient Method, Thirty-ninth International Conference on Machine Learning(ICML2022), paper link is https://icml.cc/Conferences/2022/Schedule?showEvent=16955.
  • Akihiro Yoshida, Haruki Sato, Shiori Uchiumi, Nariaki Tateiwa, Daisuke Kataoka, Akira Tanaka, Nozomi Hata, Yousuke Yatsushiro, Ayano Ide, Hiroki Ishikura, Shingo Egi, Miyu Fujii, Hiroki Kai, Katsuki Fujisawa, Long-Term Optimal Delivery Planning for Replacing the Liquefied Petroleum Gas Cylinder, arxiv preprint, paper link is here.

Portfolios

Task Assignment Problem The source code can also be found here. Software Design 2021ๅนด12ๆœˆๅท Software Design 2021ๅนด11ๆœˆๅท Portofolio Optmization CUDRA, released: Oct 16, 2021.
A Python library for the job scheduler when using cuda.

Presentation

ใƒ‡ใ‚ธใ‚ฟใƒซใƒปๆ—ฅๆœฌใฎๅฎŸ็พใซๅ‘ใ‘ใŸใƒ‡ใƒผใ‚ฟๆ ผไป˜ใ‘ๆ•ฐ็†ๅŸบ็›คใซ้–ขใ™ใ‚‹ใ‚ทใƒณใƒใ‚ธใ‚ฆใƒ , Dec.2020

Participation

The first public seminar "Science Cafe", 17.Sep.2021

News

NISTใŒ้–‹ๅ‚ฌใ—ใŸๆ˜ ๅƒ่งฃๆžใ‚ณใƒณใƒ†ใ‚นใƒˆใ€ŒTRECVID 2023ใ€ใฎๆ˜ ๅƒๆคœ็ดข้ƒจ้–€ใจๆ˜ ๅƒ่ชฌๆ˜Žๆ–‡็”Ÿๆˆ้ƒจ้–€ใงไธ–็•Œๆœ€้ซ˜ๆฐดๆบ–ใฎ็ฒพๅบฆใ‚’้”ๆˆ

ใ‚ฝใƒ•ใƒˆใƒใƒณใ‚ฏใจไนๅทžๅคงๅญฆใŒใ€LPใ‚ฌใ‚นๅฎนๅ™จใฎ้…้€ๆœ€้ฉๅŒ–ใฎๅ…ฑๅŒ็ ”็ฉถใ‚’ๅฎŸๆ–ฝ

Experience

Internship in Panasonic Corporation [12.Feb. 2019 - 22.Feb. 2019 in Osaka, Japan].
Software Development aboud mathematical optimization using Simulated Annealing(SA).

Amplify Apps Developer in Fixstars Corporation [Feb.2021 - present in Tokyo, Japan]. Application Development aboud Amplify Annealing Engine using Amplify SDK.

Patents

็‰น้ก˜2023-157707 (ๅ‡บ้ก˜ไธญ)

็‰น้ก˜2021-136392 (ๅ…ฌ้–‹ๆธˆใฟ): https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202303009118398320

Collaborator

Nariaki Tateiwa: https://nariaki3551.github.io/index.html

GitHub Stats

Anurag's GitHub stats

Top Langs

WakaTime Stats

Lines of code

๐Ÿฑ My GitHub Data

๐Ÿ† 1,512 Contributions in the Year 2021

๐Ÿ“ฆ 72.4 kB Used in GitHub's Storage

๐Ÿšซ Not Opted to Hire

๐Ÿ“œ 55 Public Repositories

๐Ÿ”‘ 6 Private Repositories

I'm a Night ๐Ÿฆ‰

๐ŸŒž Morning    58 commits     โ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   9.95% 
๐ŸŒ† Daytime    210 commits    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   36.02% 
๐ŸŒƒ Evening    275 commits    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   47.17% 
๐ŸŒ™ Night      40 commits     โ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   6.86%

๐Ÿ“… I'm Most Productive on Friday

Monday       97 commits     โ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   16.64% 
Tuesday      86 commits     โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   14.75% 
Wednesday    82 commits     โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   14.07% 
Thursday     82 commits     โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   14.07% 
Friday       144 commits    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   24.7% 
Saturday     61 commits     โ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   10.46% 
Sunday       31 commits     โ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   5.32%

๐Ÿ“Š This Week I Spent My Time On

โŒš๏ธŽ Time Zone: Asia/Tokyo

๐Ÿ’ฌ Programming Languages: 
Other                    40 hrs 27 mins      โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘   90.01% 
Python                   3 hrs 37 mins       โ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   8.08% 
Markdown                 22 mins             โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   0.82% 
YAML                     14 mins             โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   0.53% 
CSV                      6 mins              โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   0.23%

๐Ÿ”ฅ Editors: 
Browser                  38 hrs 7 mins       โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘   84.82% 
VS Code                  6 hrs 48 mins       โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   15.14% 
Vim                      1 min               โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   0.04%

๐Ÿ’ป Operating System: 
Mac                      39 hrs 7 mins       โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘   87.06% 
Linux                    5 hrs 49 mins       โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   12.94%

I Mostly Code in Python

Python                   17 repos            โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   56.67% 
Jupyter Notebook         5 repos             โ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   16.67% 
C++                      1 repo              โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   3.33% 
HTML                     1 repo              โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   3.33% 
Rich Text Format         1 repo              โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘   3.33%

Timeline

Chart not found

Last Updated on 28/11/2021

Haruki Sato's Projects

attgan-pytorch icon attgan-pytorch

AttGAN PyTorch Arbitrary Facial Attribute Editing: Only Change What You Want

auto-attack icon auto-attack

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

cocoapi icon cocoapi

COCO API - Dataset @ http://cocodataset.org/

decomoji icon decomoji

Custom icon collection for slack reaction

deepmind-research icon deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

detr icon detr

End-to-End Object Detection with Transformers

edgefool icon edgefool

PyTorch implementation of EdgeFool: An Adversarial Image Enhancement Filter, ICASSP2020

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fastgan-pytorch icon fastgan-pytorch

Official implementation of the paper "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis" in ICLR 2021

flopt icon flopt

A python Non-Linear Programming API with Heuristic approach

fx icon fx

LSTMใฎๅฎŸ่ฃ….

git_tutorial icon git_tutorial

git / githubใซใชใ‚Œใ‚‹ใŸใ‚ใฎใƒฌใƒใ‚ธใƒˆใƒชใงใ™

html icon html

gitใฎใƒฌใ‚ฏใƒใƒฃ

jecon-bst icon jecon-bst

jecon.bst๏ผˆ็ตŒๆธˆๅญฆ็”จใฎBibTeX style file๏ผ‰

kornia icon kornia

Open Source Differentiable Computer Vision Library for PyTorch

lightweight-gan icon lightweight-gan

Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

ludwig icon ludwig

Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

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