TT-NF: Tensor Train Neural Fields

Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations. In this paper, we introduce a novel low-rank representation termed Tensor Train Neural Fields (TT-NF) for learning neural fields on dense regular grids and efficient methods for sampling from them. Our representation is a TT parameterization of the neural field, trained with backpropagation to minimize a non-convex objective. We analyze the effect of low-rank compression on the downstream task quality metrics in two settings. First, we demonstrate the efficiency of our method in a sandbox task of tensor denoising, which admits comparison with SVD-based schemes designed to minimize reconstruction error. Furthermore, we apply the proposed approach to Neural Radiance Fields, where the low-rank structure of the field corresponding to the best quality can be discovered only through learning.Comment: Preprint, under revie

Power System Simulation, Control and Optimization

This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence

Efficient Fully-Convolutional Networks for Image Perception

Neural architecture search is widely applied to design networks to outperform manually designed architectures. However, it is not trivial to be directly applied to challenging perception tasks such as object detection since previous methods often rely on manually designed complex operations such as RoI pooling and RCNN heads. Thus, we look for universal fully-convolutional representations for perception tasks, which are easy to optimise and deploy because of their sim ple structures. They perform well on dense prediction tasks such as semantic segmentation, where the networks consist of a backbone module for visual feature extraction and a task-specific module for result generation. Designing the task-specific modules helps us understand how these networks tackle perception tasks and is also crucial for performance and efficiency improvements. However, fully-convolutional networks fall behind two-stage approaches on instance-level tasks such as object detection and instance segmentation. To solve this problem, we focus on designing fully-convolutional frameworks for instance detection tasks and study the task-specific structures and improve their performance by devising efficient neural architecture search algorithms. Our approach starts by designing fully-convolutional models for instance detection tasks. With de- formable convolution, we tackle the local-incoherence problem for top-down instance segmentation, resulting in a fully-convolutional model with equivalent expressiveness as a typical two-stage model. We also propose BlendMask, a fully-convolutional instance segmentation network that is faster and more ac- curate than the state-of-the-art two-stage models. Then we demonstrate the benefit of having uniform representation by designing the first a panoptic segmentation network solving instance and semantic segmentation with a single branch. Targeting to improve the design of task-specific modules for fully- convolutional perception models, we devised efficient neural architecture search algorithms and applied them to video segmentation and object detection.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Ubiquitous volume rendering in the web platform

176 p.The main thesis hypothesis is that ubiquitous volume rendering can be achieved using WebGL. The thesis enumerates the challenges that should be met to achieve that goal. The results allow web content developers the integration of interactive volume rendering within standard HTML5 web pages. Content developers only need to declare the X3D nodes that provide the rendering characteristics they desire. In contrast to the systems that provide specific GPU programs, the presented architecture creates automatically the GPU code required by the WebGL graphics pipeline. This code is generated directly from the X3D nodes declared in the virtual scene. Therefore, content developers do not need to know about the GPU.The thesis extends previous research on web compatible volume data structures for WebGL, ray-casting hybrid surface and volumetric rendering, progressive volume rendering and some specific problems related to the visualization of medical datasets. Finally, the thesis contributes to the X3D standard with some proposals to extend and improve the volume rendering component. The proposals are in an advance stage towards their acceptance by the Web3D Consortium

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Automatic machine learning:methods, systems, challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself