ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
This document summarizes a research paper on scaling laws for neural language models. Some key findings of the paper include:
- Language model performance depends strongly on model scale and weakly on model shape. With enough compute and data, performance scales as a power law of parameters, compute, and data.
- Overfitting is universal, with penalties depending on the ratio of parameters to data.
- Large models have higher sample efficiency and can reach the same performance levels with less optimization steps and data points.
- The paper motivated subsequent work by OpenAI on applying scaling laws to other domains like computer vision and developing increasingly large language models like GPT-3.
The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
KDD Cup 2021で開催された時系列異常検知コンペ
Multi-dataset Time Series Anomaly Detection (https://compete.hexagon-ml.com/practice/competition/39/) に参加して
5位入賞した解法の紹介と上位解法の整理のための資料です.
9/24のKDD2021参加報告&論文読み会 (https://connpass.com/event/223966/) の発表資料です.
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
Docker is all the rage these days. While one doesn't hear much about Solr on Docker, we're here to tell you not only that it can be done, but also share how it's done.
We'll quickly go over the basic Docker ideas - containers are lighter than VMs, they solve "but it worked on my laptop" issues - so we can dive into the specifics of running Solr on Docker.
We'll do a live demo showing you how to run Solr master - slave as well as SolrCloud using containers, how to manage CPU assignments, constraint memory and use Docker data volumes when running Solr in containers. We will also show you how to create your own containers with custom configurations.
Finally, we'll address one of the core Solr questions - which deployment type should I use? We will demonstrate performance differences between the following deployment types:
- Single Solr instance running on a bare metal machine
- Multiple Solr instances running on a single bare metal machine
- Solr running in containers
- Solr running on virtual machine
- Solr running on virtual machine using unikernel
For each deployment type we'll address how it impacts performance, operational flexibility and all other key pros and cons you ought to keep in mind.
JavaOne 2017 報告会 at Japan Java User Group
デモのコード:https://github.com/ykubota/jigsaw-sample_jp
イベントページ:https://jjug.doorkeeper.jp/events/66256
Stream: https://www.youtube.com/watch?v=XT2tIh9r6Eo
slideshareが自動的にPDFに変換するように仕様変更されていたため、ノート付きでアップロードができませんでした。お手数をおかけしますが、原稿(簡単ですが…)を読んでみたい方は筆者までTwitterでDMかメールなどでご連絡お願いします。
This document discusses several popular Java libraries including:
- Dependency injection frameworks like Guice and Spring
- Logging with SLF4J
- Collections and utilities with Guava
- HTTP clients like OkHttp
- Reactive programming with RxJava
- REST with Retrofit
- Asynchronous programming with JDeferred
- Event handling with MBassador
- Code generation with Lombok and ByteBuddy
- Testing utilities like JUnitParams, Mockito, Jukito, and Spock
- Waiting assertions with Awaitility and REST testing with Rest-assured.
Paper Introduction "RankCompete:Simultaneous ranking and clustering of info...Kotaro Yamazaki
Paper Introduction.
RankCompete:Simultaneous ranking and clustering of information networks
https://www.researchgate.net/publication/257352130_RankCompete_Simultaneous_ranking_and_clustering_of_information_networks
91. 实装
• RankingSVM
– svm_rank by T. Joachims
• http://www.cs.cornell.edu/People/tj/svm_light/svm_rank.html
• Stochastic Pairwise Descent
– sofia-ml by D. Sculley
• http://code.google.com/p/sofia-ml/
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92. Learning to Rank教科書
• Tie-Yan Liu. Learning to Rank for Information Retrieval.
Springer (2011).
• Tie-Yan Liu. Learning to Rank for Information Retrieval
(Foundations and Trends(R) in Information Retrieval), Now
Publishers (2009)
• Hang Li, Learning to Rank for Information Retrieval and
Natural Language Processing, Morgan & Claypool (2011)
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93. 情報検索の教科書
• Christopher D. Manning, Prabhakar Raghavan, Hinrich Schuetze, “Introduction
to Information Retrieval”, Cambridge University Press (2008).
– Webで全ページ公開されている.情報検索全般的にバランスよく書かれている
• Bruce Croft, Donald Metzler, Trevor Strohman, “Search Engines: Information
Retrieval in Practice”, Pearson Education (2009).
– 検索エンジン寄りの話.エンジニア向けに書かれている.一番簡単かも.
• Stefan Buttcher, Charles L. A. Clarke and Gordon V. Cormack, “Information
Retrieval”, The MIT Press, 2010.
– 实装から理論まで王道を押さえてしっかり書かれている印象.特にお薦め.
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94. チュートリアル資料
• Tie Yan Liu. Learning to Rank for Information Retrieval. SIGIR ‘08
Tutorial.
– http://research.microsoft.com/en-us/people/tyliu/letor-tutorial-
sigir08.pdf
• Hang Li. Learning to Rank. ACL-IJCNLP ‘09 Tutorial.
– http://research.microsoft.com/en-us/people/hangli/li-acl-ijcnlp-2009-
tutorial.pdf
• Shivani Agarwal. Ranking Methods in Machine Learning, SDM ’10
Tutorial.
– http://web.mit.edu/shivani/www/Events/SDM-10-Tutorial/sdm10-
tutorial.pdf
• 徳永拓之. Confidence Weightedでランク学習を实装してみた.
TokyoNLP#4 (2011).
– http://www.slideshare.net/tkng/confidence-weighted
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