Regularization plays a vital role in the context of deep learning by preventing deep neural networks from the danger of overfitting. This paper proposes a novel deep learning regularization method named as DL-Reg, which carefully reduces the nonlinearity of deep networks to a certain extent by explicitly enforcing the network to behave as much linear as possible. The key idea is to add a linear constraint to the objective function of the deep neural networks, which is simply the error of a linear mapping from the inputs to the outputs of the model. More precisely, the proposed DL-Reg carefully forces the network to behave in a linear manner. This linear constraint, which is further adjusted by a regularization factor, prevents the network from the risk of overfitting. The performance of DL-Reg is evaluated by training state-of-the-art deep network models on several benchmark datasets. The experimental results show that the proposed regularization method: 1) gives major improvements over the existing regularization techniques, and 2) significantly improves the performance of deep neural networks, especially in the case of small-sized training datasets.

The Travelling Salesman Problem (TSP) is a classical combinatorial optimisation problem. Deep learning has been successfully extended to meta-learning, where previous solving efforts assist in learning how to optimise future optimisation instances. In recent years, learning to optimise approaches have shown success in solving TSP problems. However, they focus on one type of TSP problem, namely ones where the points are uniformly distributed in Euclidean spaces and have issues in generalising to other embedding spaces, e.g., spherical distance spaces, and to TSP instances where the points are distributed in a non-uniform manner. An aim of learning to optimise is to train once and solve across a broad spectrum of (TSP) problems. Although supervised learning approaches have shown to achieve more optimal solutions than unsupervised approaches, they do require the generation of training data and running a solver to obtain solutions to learn from, which can be time-consuming and difficult to find reasonable solutions for harder TSP instances. Hence this paper introduces a new learning-based approach to solve a variety of different and common TSP problems that are trained on easier instances which are faster to train and are easier to obtain better solutions. We name this approach the non-Euclidean TSP network (NETSP-Net). The approach is evaluated on various TSP instances using the benchmark TSPLIB dataset and popular instance generator used in the literature. We performed extensive experiments that indicate our approach generalises across many types of instances and scales to instances that are larger than what was used during training.

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to effectively utilize relevant domain knowledge and experience critical to human understanding. This aspect is missing in early data-focused approaches and necessitated knowledge-infused learning and other strategies to incorporate computational knowledge. This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL methods using knowledge-infused learning, which is one of the strategies. We then discuss how this makes a fundamental difference in the interpretability and explainability of current approaches, and illustrate it with examples from natural language processing for healthcare and education applications.

The Hoover Institution's Condoleezza Rice and HAI Co-Director Fei-Fei Li discuss AI ethics, technology advances, and the...

MaskedFace-Net is a dataset of human faces with a correctly or incorrectly worn mask (137,016 images) based on the dataset Flickr-Faces-HQ (FFHQ). The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature.

As the coronavirus pandemic accelerates the automation of the retail industry, Ocado Group PLC (LON:OCDO) has stepped up its investment in robotics and machine learning. The FTSE 100 group is now buying a company that specialises in an issue that Amazon's Jeff Bezos has several times stated as perhaps the most difficult and last remaining element in the race to automate the retail industry. Ocado has agreed to buy Kindred Systems Inc, a US company specialising in'piece picking' robots, for roughly US$262mln. Using automated intelligence (AI) and deep learning, robots from Kindred and its rivals are increasingly being used by retail and logistics companies to achieve Bezos's tricky task of picking up and moving items without breaking them. Kindred robots use automated intelligence (AI) to power their vision and motion control, while the piece-picking arms are developed using'deep reinforcement learning', a form of AI that improves the learning process for robots handling a wide variety of large, small, hard and soft items such as in grocery.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons.

The market research report on the Global Deep Learning Market has been formulated through a series of extensive primary and secondary research approaches. The data is further verified and validated by industry experts and professionals. The forecast for 2020-2027 has been covered in the report and offers an extensive historical analysis for the key segments of the Deep Learning market. The well-formulated research report aims to provide the readers with a better understanding of the industry and help them formulate strategic investment plans. The report also evaluates the market dynamics, including drivers, restraints, opportunities, threats, challenges, and other key segments.

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs).

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