Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allows optimization of their parameters to be efficiently performed via maximum likelihood. However, data of interest is typically assumed to live in some (often unknown) low-dimensional manifold embedded in high-dimensional ambient space. The result is a modelling mismatch since -- by construction -- the invertibility requirement implies high-dimensional support of the learned distribution. Injective flows, mapping from low- to high-dimensional space, aim to fix this discrepancy by learning distributions on manifolds, but the resulting volume-change term becomes more challenging to evaluate. Current approaches either avoid computing this term entirely using various heuristics, or assume the manifold is known beforehand and therefore are not widely applicable. Instead, we propose two methods to tractably calculate the gradient of this term with respect to the parameters of the model, relying on careful use of automatic differentiation and techniques from numerical linear algebra. Both approaches perform end-to-end nonlinear manifold learning and density estimation for data projected onto this manifold. We study the trade-offs between our proposed methods, empirically verify that we outperform approaches ignoring the volume-change term by more accurately learning manifolds and the corresponding distributions on them, and show promising results on out-of-distribution detection.

IMAGE: Researchers from the Institute of Industrial Science at The University of Tokyo, Kobe Steel, Ltd, and Kobelco Research Institute, Inc, develop high-density, energy-efficient 3D embedded RAM for artificial intelligence applications.... view more Tokyo - Machine learning is the process by which computers adapt their responses without human intervention. This form of artificial intelligence (AI) is now common in everyday tools such as virtual assistants and is being developed for use in areas from medicine to agriculture. A challenge posed by the rapid expansion of machine learning is the high energy demand of the complex computing processes. Researchers from The University of Tokyo have reported the first integration of a mobility-enhanced field-effect transistor (FET) and a ferroelectric capacitor (FE-CAP) to bring the memory system into the proximity of a microprocessor and improve the efficiency of the data-intensive computing system. Their findings were presented at the 2021 Symposium on VLSI Technology.

The event chaired by Mark Maybury (Chief Technology Officer, Stanley Black & Decker, mark.maybury@sbdinc.com) From steam power and electrification in the first industrial revolution to assembly line driven mass production of the second industrial revolution to computerization in the third industrial revolution, disruptive innovations have driven key change including urbanization, global travel, and information discovery and sharing. Equally if not more profoundly, the current cyber-physical fourth industrial transformation is driving fundamental changes not only in the way we manufacture but also because of the kinds of products and services created ways in which we live, work, and play. Studies from intelligent manufacturing experts at the World Economic Forum have identified a set of key foundational elements for Industry 4.0. These include the Internet of Things (IOT), big data, cloud computing additive manufacturing, augmented reality, autonomous robots, and modeling and simulation.

With schools reopening worldwide, Google has worked hard to ensure the big market gains it made in 2020 can be sustained and strengthened as students return to physical rather than virtual classrooms. With user numbers of its digital learning platform, Google Classroom, up to 150 million from 40 million just a year before, it announced a new "road map" for the platform in early 2021. "As more teachers use Classroom as their'hub' of learning during the pandemic, many schools are treating it as their learning management system (LMS)," wrote Classroom's program manager. "While we didn't set out to create an LMS, Classroom is committed to meeting the evolving needs of schools." The road map for Classroom as a school LMS was just one plan laid out at its annual Learning with Google conference, which also included the launch of 40 new Chromebook laptop models alongside feature upgrades across its educational products.

Training Classes are 8 hours, focused, deep-dive, demo-based virtual classroom training. Each class will run for eight hours in total, four hours each day, for two consecutive days. There will be three batches spread across three different time zones. Each batch will have four parallel classes. Click Here to learn about our global delivery model. Each Training Class is designed to offer intermediate & advanced-level training on a specific topic/subject. These classes offer more knowledge, skills, and expertise beyond the summit content. Apart from attending LIVE classes, you also get on-demand access to class recordings*, exclusive content from the instructor, and the participation certificate.

While the world is still recuperating from the pandemic, businesses, large and small, which banked on technology, were able to move on quickly with their operations. This has led to a rise in demand for tech-based job roles such as data analyst, data scientist, cloud architect, and security engineer, among others. The year 2021 is set to drive massive growth for such roles as organisations are looking to create a skilled talent pool for a better digital continuity. For those of you looking to ride the digital wave and make the best of this situation, equipping yourself with new-age skills is the key to powering ahead in your careers. If you are interested in building a career in information technology, here are the top skills you must have on your wish-list while selecting your course post-class 12.

This post covers model inference optimization or compression in breadth and hopefully depth as of March 2021. This includes engineering topics like model quantization and binarization, more research-oriented topics like knowledge distillation, as well as well-known-hacks. Each year, larger and larger models are able to find methods for extracting signal from the noise in machine learning. In particular, language models get larger every day. These models are computationally expensive (in both runtime and memory), which can be both costly when served out to customers or too slow or large to function in edge environments like a phone. Researchers and practitioners have come up with many methods for optimizing neural networks to run faster or with less memory usage.

Generalized Linear Model (GLiM, or GLM) is an advanced statistical modelling technique formulated by John Nelder and Robert Wedderburn in 1972. It is an umbrella term that encompasses many other models, which allows the response variable y to have an error distribution other than a normal distribution. The models include Linear Regression, Logistic Regression, and Poisson Regression. The underlying relationship between the response and the predictors is linear (i.e. Also, the error distribution of the response variable should be normally distributed.

Today's highlighted deal comes via our Online Courses section of the Neowin Deals store, where you can save 95% off the Ultimate Artificial Intelligence Scientist Certification Bundle. Be an in-demand, certified tech professional with 87 hours of content on Python, Tensorflow, Machine and Deep Learning. This Ultimate Artificial Intelligence Scientist Certification Bundle normally costs $799 but it can be yours for only $34.99, that's a saving of $764.01 (95%) off! That's OK, there are other deals on offer you can check out here. Disclosure: This is a StackCommerce deal or giveaway in partnership with Neowin; an account at StackCommerce is required to participate in any deals or giveaways.

Today's artificial intelligence remains a long way from the supple, dynamic intelligence of AI ... [ ] characters from popular fiction, like The Jetsons. Modern artificial intelligence is capable of wonders. It can produce breathtaking original content: poetry, prose, images, music, human faces. Last year it produced a solution to the "protein folding problem," a grand challenge in biology that has stumped researchers for half a century. Yet today's AI still has fundamental limitations. Relative to what we would expect from a truly intelligent agent--relative to that original inspiration and benchmark for artificial intelligence, human cognition--AI has a long way to go. Critics like to point to these shortcomings as evidence that the pursuit of artificial intelligence is misguided or has failed.