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.
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
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.
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
Healthy Competition: How Adversarial Reasoning is Leading the Next Wave of In...John Liu
In recent years, machine learning and reinforcement learning algorithms have revolutionized how we tackle problems in pattern recognition, inference and prediction. These learning algorithms are inherently stochastic in nature and collaborative by design. While powerful, they often lead to models that exhibit fragility in noisy real-world domains. A new generation of learning algorithms are evolving to augment robustness by embracing adversarial reasoning. In place of cooperative learning, these algorithms espouse game theoretic concepts of competition, deception, and Nash equilibria. In this talk, John will examine the role of adversarial reasoning in problem solving. Attendees will learn about the principles underpinning adversarial reasoning and their relevance to the new generation of machine learning algorithms including actor-critic A3C methods, generative adversarial networks, and variational autoencoders. In the end, the objective of this talk is to provide an intuitive understanding of the coming learning algorithms that can surmise intent, detect and practice deception, and formulate long-range winning strategies to real world problems.
This document introduces the deep reinforcement learning model 'A3C' by Japanese.
Original literature is "Asynchronous Methods for Deep Reinforcement Learning" written by V. Mnih, et. al.
PFN Spring Internship Final Report: Autonomous Drive by Deep RLNaoto Yoshida
This is the final report for the spring internship 2016 at Preferred Networks. gym_torcs is released in mt github account: https://github.com/ugo-nama-kun/gym_torcs
論文紹介:
Pan, Wei-Xing, et al. "Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network." The Journal of neuroscience 25.26 (2005): 6235-6242.
Sotetsu Koyamada (Presenter), Masanori Koyama, Ken Nakae, Shin Ishii
Graduate School of Informatics, Kyoto University
[Abstract]
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA’s ability to decompose the knowledge acquired by the trained classifiers.
[Keywords]
Sensitivity analysis Sensitivity map PCA Dark knowledge Knowledge decomposition
@PAKDD2015
May 20, 2015
Ho Chi Minh City, Viet Namﳟ
http://link.springer.com/chapter/10.1007%2F978-3-319-18038-0_48#page-1
Li, Mu, et al. "Efficient mini-batch training for stochastic optimization." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.
http://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf
KDD2014勉強会関西会場: http://www.ml.ist.i.kyoto-u.ac.jp/kdd2014reading