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.
文献紹介:Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Re...Toru Tamaki
Chun-Fu Richard Chen, Rameswar Panda, Kandan Ramakrishnan, Rogerio Feris, John Cohn, Aude Oliva, Quanfu Fan; Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 6165-6175
https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Deep_Analysis_of_CNN-Based_Spatio-Temporal_Representations_for_Action_Recognition_CVPR_2021_paper.html
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.
文献紹介:Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Re...Toru Tamaki
Chun-Fu Richard Chen, Rameswar Panda, Kandan Ramakrishnan, Rogerio Feris, John Cohn, Aude Oliva, Quanfu Fan; Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 6165-6175
https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Deep_Analysis_of_CNN-Based_Spatio-Temporal_Representations_for_Action_Recognition_CVPR_2021_paper.html
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
文献紹介:Gate-Shift Networks for Video Action RecognitionToru Tamaki
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz; Gate-Shift Networks for Video Action Recognition, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1102-1111
https://openaccess.thecvf.com/content_CVPR_2020/html/Sudhakaran_Gate-Shift_Networks_for_Video_Action_Recognition_CVPR_2020_paper.html
文献紹介:Token Shift Transformer for Video ClassificationToru Tamaki
Hao Zhang, Yanbin Hao, Chong-Wah Ngo, Token Shift Transformer for Video Classification, ACM MM '21: Proceedings of the 29th ACM International Conference on MultimediaOctober 2021 Pages 917–925https://doi.org/10.1145/3474085.3475272
http://vireo.cs.cityu.edu.hk/papers/Hao_MM2021.pdf
http://arxiv.org/abs/2108.02432
https://dl.acm.org/doi/abs/10.1145/3474085.3475272
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
研究室のゼミの論文紹介の発表資料です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803–827. doi: 10.1002/rob.20204
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
Machine Learning for Molecular Graph Representations and GeometriesIchigaku Takigawa
Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
2. GeometricalandTopologicalRepresentationLearning
https://gt-rl.github.io/cfp
Scope and topics
• Applications of geometry- or topology-based models
• Approximation schemes in topological data analysis
• Big data and scalability aspects
• Equivariant neural networks
• Graph representation learning
• Higher-order features of unstructured and structured data sets
• Manifold learning at scale
• Message passing and beyond
• New datasets and benchmarks
• Topological machine learning
1. 幾何的深層学習とGraph Neural Networks
2. トポロジカルデータ解析
3. 多様体学習
2は⻘⽊さんが解説されるので詳細省略!
8. 2.対象が幾何構造上に分布する
ICLR Computational Geometry & Topology Challenge 2022
https://github.com/geomstats/challenge-iclr-2022
The purpose of this challenge is to foster reproducible research in geometric (deep) learning, by crowdsourcing
the open-source implementation of learning algorithms on manifolds. Participants are asked to contribute
code for a published/unpublished algorithm, following Scikit-Learn/Geomstats' or pytorch's APIs and
computational primitives, benchmark it, and demonstrate its use in real-world scenarios.
何でも良いから実装を実問題のユースケースでデモしあうクラウドソーシングで知⾒収集
(賞⾦:1位 $2000, 2位 $1000, 3位 $500)
Geomstatsというパッケージ(後述)のgithub repoにプルリクを送る形でホストされている
11. 2.対象が幾何構造上に分布する
• Geomstats: A Python Package for Riemannian Geometry in Machine Learning (2020)
https://arxiv.org/abs/2004.04667
12. 2.対象が幾何構造上に分布する
• Geomstats: A Python Package for Riemannian Geometry in Machine Learning (2020)
https://arxiv.org/abs/2004.04667
13. トポロジカルデータ解析(TDA)for1.対象が幾何構造を持つ
• giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration (2020)
https://arxiv.org/abs/2004.02551
Persistence diagramsによるTDAをsklearnで使いやすく
20. 参考:ガラスとGraphNeuralNetworks
Bapst, V., Keck, T., Grabska-Barwińska, A. et al. Unveiling the predictive power of static structure in glassy systems.
Nat. Phys. 16, 448–454 (2020). https://doi.org/10.1038/s41567-020-0842-8
https://www.deepmind.com/blog/towards-understanding-glasses-with-graph-neural-networks
28. MolecularGraphs
1. 2.
1 2 1 0
2 3 12 0
3 4 12 0
4 5 12 0
5 6 12 0
6 7 12 0
7 8 2 0
7 9 12 0
9 10 12 0
10 11 2 0
10 12 12 0
12 13 1 0
9 14 1 0
6 2 12 0
12 5 12 0
1. 2. 3. 4. 5. 6.
1 C 6 4 3 0 12.011 0
2 N 7 3 0 0 14.007 1
3 C 6 3 1 0 12.011 1
4 N 7 2 0 0 14.007 1
5 C 6 3 0 0 12.011 1
6 C 6 3 0 0 12.011 1
7 C 6 3 0 0 12.011 1
8 O 8 1 0 0 15.999 0
9 N 7 3 0 0 14.007 1
10 C 6 3 0 0 12.011 1
11 O 8 1 0 0 15.999 0
12 N 7 3 0 0 14.007 1
13 C 6 4 3 0 12.011 0
14 C 6 4 3 0 12.011 0
8
11
12
2
9
4
6
5
7
10
3
13
1
14
Node features Edge features
1. Atomic number
2. # of directly-bonded neighbors
3. # of hydrogens
4. Formal charge
5. Atomic mass
6. Is in a ring?
1. Bond type
2. Stereochemistry
29. GeometricGraphs
Non-geometric node features
Non-geometric edge features
+ Can be added
GraphといいつつPoint Set
として扱う(エッジは明⽰的には
⼊⼒しない)場合が多い
完全グラフを考える(Transformer),
cut offは頂点間距離で内部的に定義,
etc
Graph in Euclid Space
<latexit sha1_base64="T9NBOfd7zcDksHyg6Y5oyIVx9gw=">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</latexit>
{xi 2 R3
: i = 1, . . . , n}
30. GraphNeuralNetworksとMessagePassing
GNN
Layer
1
3
2 4
1
2 3
4
5
6
1 2 3 4
1 2 3 4 5 6
Node
features
Edge
features
1
3
2 4
1
2 3
4
5
6
1 2 3 4
1 2 3 4 5 6
Global
Pooling
(Readout)
Graph-level
Prediction
Node-level
Prediction
Edge-level
Prediction
Update
Update
Head
Head
Head
× Layers
Derrow-Pinion A, She J, Wong D,
Lange O, Hester T, Perez L, et al.
ETA Prediction with Graph Neural
Networks in Google Maps.
CIKM 2021
Fang X, Huang J, Wang F, Zeng L,
Liang H, Wang H. ConSTGAT:
Contextual Spatial-Temporal Graph
Attention Network for Travel Time
Estimation at Baidu Maps.
KDD 2020
Dong XL, He X, Kan A, Li X, Liang
Y, Ma J, et al. AutoKnow: Self-
Driving Knowledge Collection for
Products of Thousands of
Types.
KDD 2020
Dighe P, Adya S, Li N,
Vishnubhotla S, Naik D,
Sagar A, et al. Lattice-Based
Improvements for Voice
Triggering Using Graph
Neural Networks.
ICASSP 2020
Travel Time Estimation (Google Maps, Baidu Maps) Siri Triggering (Apple) Knowledge Collection (Amazon)