To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance. However, how much and when an ML model should be compressed, and {\em where} its training should be executed, are hard decisions to make, as they depend on the model itself, the resources of the available nodes, and the data such nodes own. Existing studies focus on each of those aspects individually, however, they do not account for how such decisions can be made jointly and adapted to one another. In this work, we model the network system focusing on the training of DNNs, formalize the above multi-dimensional problem, and, given its NP-hardness, formulate an approximate dynamic programming problem that we solve through the PACT algorithmic framework. Importantly, PACT leverages a time-expanded graph representing the learning process, and a data-driven and theoretical approach for the prediction of the loss evolution to be expected as a consequence of training decisions. We prove that PACT's solutions can get as close to the optimum as desired, at the cost of an increased time complexity, and that, in any case, such complexity is polynomial. Numerical results also show that, even under the most disadvantageous settings, PACT outperforms state-of-the-art alternatives and closely matches the optimal energy cost.

In a recent paper Wunderlich and Pehle introduced the EventProp algorithm that enables training spiking neural networks by gradient descent on exact gradients. In this paper we present extensions of EventProp to support a wider class of loss functions and an implementation in the GPU enhanced neuronal networks framework which exploits sparsity. The GPU acceleration allows us to test EventProp extensively on more challenging learning benchmarks. We find that EventProp performs well on some tasks but for others there are issues where learning is slow or fails entirely. Here, we analyse these issues in detail and discover that they relate to the use of the exact gradient of the loss function, which by its nature does not provide information about loss changes due to spike creation or spike deletion. Depending on the details of the task and loss function, descending the exact gradient with EventProp can lead to the deletion of important spikes and so to an inadvertent increase of the loss and decrease of classification accuracy and hence a failure to learn. In other situations the lack of knowledge about the benefits of creating additional spikes can lead to a lack of gradient flow into earlier layers, slowing down learning. We eventually present a first glimpse of a solution to these problems in the form of `loss shaping', where we introduce a suitable weighting function into an integral loss to increase gradient flow from the output layer towards earlier layers.

A deformable land-air robot is introduced with excellent driving and flying capabilities, offering a smooth switching mechanism between the two modes. An elaborate coupled dynamics model is established for the robot, including rotors, chassis, suspension, and the deformable structure. In addition, a model-based controller is designed for landing and mode switching in various unstructured conditions, such as slopes and curved surface. And considering locomotion and complex near-ground situations to achieve cooperation between the two fused modalities. This system was simulated in ADAMS/Simulink and a tested with hardware-in-the-loop system was constructed for testing in various slopes. With a designed controller, the results showed the robot is capable of fast and smooth land-air switching, with a 24.6 % faster landing on slopes. The controller can also reduce landing offset and impact force more effectively than the normal control method at 32.7 % and 34.3 %, respectively.

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.

Growing the business and evolving as the market leader has always been about innovation in functionalities of employee management, customer experience, and others. Companies bring the desired changes in their business operations, services, and support by leveraging machine learning and deep learning solutions. Deep learning and machine learning have become the new face of growth and success in recent years. Companies increasingly use deep learning and machine learning solutions to innovate different aspects of their business. The corporate sector expects revenue of $59.8 billion with AI (Artificial Intelligence) and ML (Machine Learning) by 2025. Machine Learning (ML) and Deep Learning (DL) let the corporates drive informed decisions towards digital transformation without making errors and taking risks.

Do you want to upgrade your skills with Best Data Analytics Certification Online to stand out in the industry? Here is a list of Best Data Analytics Courses Online, Training, Tutorials, and Classes to assist you to become a top Data Analyst. Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subjects in every sector for almost every industry. Learn business analytics to get hands-on knowledge of big data analytics, data visualization, data management, and data mining as an analytics professional. The majority of business professionals are upgrading their skills with Best Data Analytics Training to stand out in their industry.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

The Department of Computer Science, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, is looking for a Doctoral student in computing science with a focus on responsible AI with learning from multiple representations. The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 60 Doctoral students within the department consists of a diverse group from different nationalities, background and fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. Is this interesting for you?

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. When beginning your educational path, it's important to first understand how to learn ML. We've broken the learning process into four areas of knowledge, with each area providing a foundational piece of the ML puzzle. To help you on your path, we've identified books, videos, and online courses that will uplevel your abilities, and prepare you to use ML for your projects.

The vast potential of Artificial Intelligence has hit a bump in the road following the refusal by several countries to patent inventions generated by an AI machine, says Macquarie Law School's Dr Rita Matulionyte. Macquarie Law School's Dr Rita Matulionyte, an international expert in intellectual property law, says the decisions – which have been challenged in overseas courts – signal a need for reform to ensure current law does not stifle innovation in an'immensely promising' sector. "This is the first case where an applicant is trying to patent AI-generated inventions and indicate AI as the inventor, whereas previously, patents granted over such AI inventions mentioned human beings as the inventors," Matulionyte says. The economic contribution of AI is potentially huge: according to Australia's AI roadmap, digital technologies including AI are potentially worth $A315 billion to Australia's economy by 2028, while AI alone could be worth $A22.17 Used by companies such as Coca-Cola and KFC to protect their secret recipes, trade secrets is another type of intellectual property law that could be available to AI inventions, Matulionyte says.