Ensemble approaches are commonly used techniques to improving a system by combining multiple model predictions. Additionally these schemes allow the uncertainty, as well as the source of the uncertainty, to be derived for the prediction. Unfortunately these benefits come at a computational and memory cost. To address this problem ensemble distillation (EnD) and more recently ensemble distribution distillation (EnDD) have been proposed that compress the ensemble into a single model, representing either the ensemble average prediction or prediction distribution respectively. This paper examines the application of both these distillation approaches to a sequence prediction task, grammatical error correction (GEC). This is an important application area for language learning tasks as it can yield highly useful feedback to the learner. It is, however, more challenging than the standard tasks investigated for distillation as the prediction of any grammatical correction to a word will be highly dependent on both the input sequence and the generated output history for the word. The performance of both EnD and EnDD are evaluated on both publicly available GEC tasks as well as a spoken language task.

Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR). There are many heuristics for IR, including visitation counts, curiosity, and state-difference. In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR. The criterion helps the agent explore Beyond the Boundary of explored regions and mitigates common issues in count-based methods, such as short-sightedness and detachment. The resulting method, BeBold, solves the 12 most challenging procedurally-generated tasks in MiniGrid with just 120M environment steps, without any curriculum learning. In comparison, the previous SoTA only solves 50% of the tasks. BeBold also achieves SoTA on multiple tasks in NetHack, a popular rogue-like game that contains more challenging procedurally-generated environments.

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item main effects, the manifest user-item interactions based on observed features, and the latent interaction effects from residuals. Unlike conventional collaborative filtering methods, the group effect of users and items are considered in GAMMLI. It is beneficial for enhancing the model interpretability, and can also facilitate the cold-start recommendation problem. A new Python package GAMMLI is developed for efficient model training and visualized interpretation of the results. By numerical experiments based on simulation data and real-world cases, the proposed method is shown to have advantages in both predictive performance and explainable recommendation.

We consider the problem of online learning in the presence of sudden distribution shifts as frequently encountered in applications such as autonomous navigation. Distribution shifts require constant performance monitoring and re-training. They may also be hard to detect and can lead to a slow but steady degradation in model performance. To address this problem we propose a new Bayesian meta-algorithm that can both (i) make inferences about subtle distribution shifts based on minimal sequential observations and (ii) accordingly adapt a model in an online fashion. The approach uses beam search over multiple change point hypotheses to perform inference on a hierarchical sequential latent variable modeling framework. Our proposed approach is model-agnostic, applicable to both supervised and unsupervised learning, and yields significant improvements over state-of-the-art Bayesian online learning approaches.

Build Python Web Applications from Beginner to Expert using Python and Flask What you'll learn Complete Python Web Course: Build … Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! Launch your business by learning to build your own eCommerce app step-by-step. The only course you need to become a full-stack web developer. This course is for PHP developers who want to use the built-in predefined variables. What you'll learn Predefined Variables in … Learn how to apply common security mitigation techniques to a web application built with Angular, Express.js

The Indian Institutes of Technology, or IITs, are 23 premiere higher education universities, with around 16,000 students, as of 2020. These are famous for their exclusivity, history, and quality of education, so it's a big deal that one of them, IIT Delhi, is launching a free online AI course. This is a 12-week course offered from 18th January 2021 to 09th April 2021. Those seeking a certificate take a proctored exam on 25th April 2021. The deadline to enroll is 25th January 2021.

Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment.

Picture this: a small group of middle school students are learning about ancient Egypt, so they strap on a virtual reality headset and, with the assistance of an artificial intelligence tour guide, begin to explore the Pyramids of Giza. The teacher, also journeying to one of the oldest known civilizations via a VR headset, has assigned students to gather information to write short essays. During the tour, the AI guide fields questions from students and points them to specific artifacts and discuss what they see. In preparing the AI-powered lesson on Egypt, the teacher beforehand would have worked with the AI program to craft a lesson plan that not only dives deep into the subject, but figures out how to keep the group moving through the virtual field trip and how to create more equal participation during the discussion. In that scenario, the AI listens, observes and interacts naturally to enhance a group learning experience, and to make a teacher's job easier.

" We will shift from a mobile first to an AI first world." Artificial intelligence (AI) is one of the most important technologies of the 21st century and part of the 4th industrial revolution. AI will transform every industry similar to electricity over 100 years ago and have a huge impact on how humans live and work in the future. Moving into Data Science is an amazing career choice. There's high demand for Data Scientists across the globe and people working in the field enjoy high salaries and rewarding careers.

For some time now, I have been wanting to write about some of the problems I observed during my time in the Learning Analytics world (which also crosses over into Artificial Intelligence, Personalization, Sentiment Analysis, and many other areas as well). I'm hesitant to do so because I know the pitchforks will come out, so I guess I should point out that all fields have problems. Even my main field of instructional design is far from perfect. Examining issues with in a field (should be) a healthy part of the growth of a field. So this will probably be a series of blog posts as I look at publications, conferences, videos, and other aspects of the LA/PA/ML/AI etc world that are in need of a critical examination.