Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
article:https://arxiv.org/abs/1907.13196v1
arXiv.org
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ --...
An almost new Reinforcement Learning specialization *_*:
https://www.coursera.org/specializations/reinforcement-learning
https://www.coursera.org/specializations/reinforcement-learning
Coursera
Reinforcement Learning
Master the Concepts of Reinforcement Learning. Implement ... Enroll for free.
Wharton's Entrepreneurship Specialization covers the conception, design, organization, and management of new enterprises. This five-course series is designed to take you from opportunity identification through launch, growth, financing and profitability. With guidance from Wharton's top professors, along with insights from current Wharton start-up founders and financiers, you'll develop an entrepreneurial mindset and hone the skills you need to develop a new enterprise with potential for growth and funding, or to identify and pursue opportunities for growth within an existing organization.
https://www.coursera.org/specializations/wharton-entrepreneurship
https://www.coursera.org/specializations/wharton-entrepreneurship
Coursera
Entrepreneurship
Offered by University of Pennsylvania. Turn Your Idea ... Enroll for free.
Artificial neural networks (ANNs) have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals (including humans), training such networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead mainly on unsupervised learning. Here we argue that most animal behavior is not the result of clever learning algorithms—supervised or unsupervised—but is encoded in the genome. Specifically, animals are born with highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is far too complex to be specified explicitly in the genome, it must be compressed through a “genomic bottleneck”. The genomic bottleneck suggests a path toward ANNs capable of rapid learning.
https://www.nature.com/articles/s41467-019-11786-6
https://www.nature.com/articles/s41467-019-11786-6
Nature
A critique of pure learning and what artificial neural networks can learn from animal brains
Nature Communications - Recent gains in artificial neural networks rely heavily on large amounts of training data. Here, the author suggests that for AI to learn from animal brains, it is important...
An insightful website which contains a history of cybernetic animals and early robots
https://cyberneticzoo.com
https://cyberneticzoo.com
Steps Toward Artificial Intelligence.pdf
5.8 MB
Discussions about several issues relevant to trial-and-error learning (Reinforcement Learning), including prediction,
expectation, and what this paper called the basic credit-assignment problem for complex reinforcement
learning systems: How do you distribute credit for success among the many
decisions that may have been involved in producing it? (Published 1961)
expectation, and what this paper called the basic credit-assignment problem for complex reinforcement
learning systems: How do you distribute credit for success among the many
decisions that may have been involved in producing it? (Published 1961)
StatisticalLearningTheory.pdf
359.2 KB
A Brief introduction to Statistical Learning theory
Computational Learning Theory versus Statistical Learning Theory
While both frameworks use similar mathematical analysis, the primary difference between CoLT and SLT are their objectives. CoLT focuses on studying “learnability,” or what functions/features are necessary to make a given task learnable for an algorithm. Whereas SLT is primarily focused on studying and improving the accuracy of existing training programs.
https://deepai.org/machine-learning-glossary-and-terms/computational-learning-theory
While both frameworks use similar mathematical analysis, the primary difference between CoLT and SLT are their objectives. CoLT focuses on studying “learnability,” or what functions/features are necessary to make a given task learnable for an algorithm. Whereas SLT is primarily focused on studying and improving the accuracy of existing training programs.
https://deepai.org/machine-learning-glossary-and-terms/computational-learning-theory
DeepAI
Computational Learning Theory
Computational Learning Theory (CoLT) is a field of AI research studying the design of machine learning algorithms to determine what sorts of problems are “learnable.”
sgd.pdf
3.9 MB
A brief introduction to stochastic approximation theory
Brains, Minds, and Machines summer course
https://www.youtube.com/watch?v=_svW8NV1A6k&list=PLUl4u3cNGP61RTZrT3MIAikp2G5EEvTjf&index=1
https://www.youtube.com/watch?v=_svW8NV1A6k&list=PLUl4u3cNGP61RTZrT3MIAikp2G5EEvTjf&index=1
YouTube
Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Tomaso Poggio Int...
How to write a book.pdf
548.2 KB
A 20 steps guide to write a book
Forwarded from Deleted Account
🔻پنجمین دورهمی مدرسه جهانی هوش مصنوعی در رشت
🔺School of AI
📘موضوع این جلسه: عصب شناسی محاسباتی و رابطه آن با هوش مصنوعی
🗓دوشنبه 11 شهریور 1398
🕐ساعت 16:30 الی 19:00
🏢 رشت، میدان انتظام (پل رازی)، پارک علم و فناوری گیلان
*جهت ثبت نام به لینک زیر مراجعه کنید:
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برای کسب اطلاعات بیشتر درباره ی این مدرسه، به کانال زیر مراجعه کنید:
🏢 @schoolofairasht
✅ @Brainandcognition_GU
🔺School of AI
📘موضوع این جلسه: عصب شناسی محاسباتی و رابطه آن با هوش مصنوعی
🗓دوشنبه 11 شهریور 1398
🕐ساعت 16:30 الی 19:00
🏢 رشت، میدان انتظام (پل رازی)، پارک علم و فناوری گیلان
*جهت ثبت نام به لینک زیر مراجعه کنید:
▪️https://evnd.co/WtokW
برای کسب اطلاعات بیشتر درباره ی این مدرسه، به کانال زیر مراجعه کنید:
🏢 @schoolofairasht
✅ @Brainandcognition_GU