Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT). This is mainly due to the compositionality of the target space, where the far-away prediction hypotheses lead to the notorious reinforced mistake problem. In this paper, we revisit the utilization of multiple diverse models and present a simple yet effective approach named Reciprocal-Supervised Learning (RSL). RSL first exploits individual models to generate pseudo parallel data, and then cooperatively trains each model on the combined synthetic corpus. RSL leverages the fact that different parameterized models have different inductive biases, and better predictions can be made by jointly exploiting the agreement among each other. Unlike the previous knowledge distillation methods built upon a much stronger teacher, RSL is capable of boosting the accuracy of one model by introducing other comparable or even weaker models. RSL can also be viewed as a more efficient alternative to ensemble. Extensive experiments demonstrate the superior performance of RSL on several benchmarks with significant margins.

Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the \emph{implicit curricula} resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of \emph{explicit curricula}, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum can indeed improve the performance either with limited training time budget or in existence of noisy data.

Online video course to teach basics for Machine Learning experiment management, pipelines automation and CI/CD to deliver ML solution into production. During these lessons you'll discover base features of Data Version Control (DVC), how it works and how it may benefit your Machine Learning and Data Science projects. During this course listeners learn engineering approaches in ML around a few practical examples. Screencast videos, repositories with examples and templates to put your hands dirty and make it easier apply best features in your own projects.

Apart from the unfortunate pandemic that struck the year 2020, it saw a lot of newer developments in the tech domain. While we covered most of these developments in the form of videos, we also introduced newer segments such as the Simulated Reality, a podcast feature by AIM and videos with renowned data scientists covering their journey in detail. This article lists 15 most popular and most viewed videos that you could binge on. Find our YouTube Channel here. One of the most viewed videos of the year; it covers the most popular technology of the year -- GPT-3.

In this column, we recount the history of EAAI (Educational Advances in Artificial Intelligence) mentored undergraduate research challenges from 2014 through the present and share a vision of how such offerings may become more diverse and engage a broader range of faculty mentors and undergraduate researchers. Unlike many academic disciplines, Computer Science undergraduate majors currently are not usually required to take or even offered a research methods course. Even so, many graduate schools desire to admit graduate students with undergraduate research experience. The EAAI Symposium has historically affirmed the value of mentored undergraduate research as an important part of undergraduate AI education. It has expressed this value through the support of a number of mentored undergraduate research challenges, described below.

If you haven't explored the RStudio website in a while, your next visit may include a pleasant surprise. I recently went to the Tidymodels page, just to see what was new and was immediately drawn into the new landscape imagined by the RStudio education team. Clicking on Get Started I came to a fork and a choice of going farther with Tidymodels or backing up a bit. I went down the beginners path Finding Your Way to R and found myself in a well-lit wood, and I was not lost. Like a park with well-marked trails, the Learning R section of the RStudio Education site branches off to R excursions graded to match the "hikers" experience.

Become an expert python Programmer and learn one of the most requested skills by Good Companies around the World. This Course will Take you from absolute beginner to Advance Level in Python Programming. Even if You have never Programmed in any language or if you already know basic python and want to learn much more deeper concepts like advance modules, multi-threading, then this course is for you. This Course is also design for those who want to learn machine learning and deep learning in future, this course will make you able to understand and write complex codes in simple way. You will learn Basics of python like Basic Datatype, Objects and Data Structure as well as you will learn advance and useful concept like, how to send emails using python script, Understanding of Decorators, implementation of Generators in order to reduce the space complexity of the Program in a simple way.

Coding is the language of the future and it has its own syntax and grammar. It is the process of creating step-by-step instructions a computer understands and needs in order for its programs to work. Learning how to code can not just help students become software engineers in the future, but rather help them develop a language skill which will be used in almost every field in future jobs. When you understand coding, you start to think and process information differently and become an active co-creator in the technology used around you. It will allow you tackle large problems by breaking them down into a sequence of smaller problems by combining maths, logic and algorithm.

The critical importance of tech skills across industries sends a clear signal: In order to meet the expectations of future administrators and employers, elementary to high school students will need to learn expertise unavailable to their parents' generation. Today's leading innovation technology is machine learning, and to address the need for this vital skill set, the latest offering from the longtime partnership of Microsoft and code.org is a new course in artificial intelligence (AI) and its societal and ethical implications designed for students in elementary and high school. AI's relevance cannot be understated, as it is the very basis for self-driving cars, but it also powers devices we've already become accustomed to, such as Amazon Alexa, interactive programming, telemed appointments, and online learning. Those are some very big responsibilities to be tackled by Gen Z. As code.org points out,despite the great benefits to society, the ethical impact can't be ignored: "How does algorithmic bias impact social justice or deep fakes impact democracy? How does society cope with rapid job automation? By learning how to consider the ethical issues that AI raises, these future computer scientists will be better able to envision the appropriate safeguards that help to maximize the benefits of AI technologies and reduce their risks."

Rank correlation is a measure of the relationship between the rankings of two variables or two rankings of the same variable. In this post, we will talk about the Spearman's rho and Kendall's tau coefficients. Let's generate 1000 observations from a multivariate normal distribution and represent the usual correlation Pearson's as well as the Rank Correlations such that the Kendall's and Spearman's As we can see, in this example the Spearman's correlation was almost identical to Pearson's, but the Kendall's was much lower. We can also do a Hypothesis testing in R for the correlation coefficient with a Null Hypothesis that there is no correlation, value is 0. In the example below, we set as an alternative hypothesis Kendall's correlation to be positive.