Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster

Generative Neural Network Approach to Designing and Optimizing Dynamic Inductive Power Transfer Systems

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge for a period of time. However, the physical designs of the charging unit coils depends on many physical parameters, which leads to complexity when determining how to design the unit. A design then needs to be judged for its quality and performance, for which there are eight proposed objective functions. These objective functions represent performance metrics but are conflicting. Some metrics, such as output power are to be maximized, while others such as stray magnetic field and volume of windings and magnetic cores are to be minimized. Different unit designs will trade off performance for these objectives. In order to address the complex issue of finding near-optimal designs, a machine-learning, Generative Neural Network (GNN) approach is presented for the rapid development of near-optimal DIPT systems. GNNs generate new examples from a trained neural network and have demonstrated remarkable power in creating novel graphic design images from text-to-image training. This stems from their ability to be creative yet constrained in a regular domain. In this case, a simulator network and evaluator generator network are implemented. The simulator is a pre-trained neural network that maps from the physical designs to the objective functions. The generator network is trained to generate the near-optimal physical designs. A design is considered successful if it passes given thresholds for each of the eight objective functions, which evaluate the quality of a design. Before training, the rate of finding successful designs is 0.005%, but within 500 training epochs the rate becomes 98% (about 30 seconds total GPU run time). Engineers and production professionals are interested in both the best performing designs as well as a diversity of configurations to build. In order to improve on these criteria, several different loss functions were developed that incorporate the objective functions. Loss functions are what the neural networks use to determine how to adjust parameters and produce a better design. The various loss functions presented greatly influence the ability of the GNN to produce diverse and high-performance design solutions

Trainable hardware for dynamical computing using error backpropagation through physical media

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers

Deep neural networks for video classification in ecology

Analyzing large volumes of video data is a challenging and time-consuming task. Automating this process would very valuable, especially in ecological research where massive amounts of video can be used to unlock new avenues of ecological research into the behaviour of animals in their environments. Deep Neural Networks, particularly Deep Convolutional Neural Networks, are a powerful class of models for computer vision. When combined with Recurrent Neural Networks, Deep Convolutional models can be applied to video for frame level video classification. This research studies two datasets: penguins and seals. The purpose of the research is to compare the performance of image-only CNNs, which treat each frame of a video independently, against a combined CNN-RNN approach; and to assess whether incorporating the motion information in the temporal aspect of video improves the accuracy of classifications in these two datasets. Video and image-only models offer similar out-of-sample performance on the simpler seals dataset but the video model led to moderate performance improvements on the more complex penguin action recognition dataset