Concrete Dropout

Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks. But to obtain well-calibrated uncertainty estimates, a grid-search over the dropout probabilities is necessary - a prohibitive operation with large models, and an impossible one with RL. We propose a new dropout variant which gives improved performance and better calibrated uncertainties. Relying on recent developments in Bayesian deep learning, we use a continuous relaxation of dropout's discrete masks. Together with a principled optimisation objective, this allows for automatic tuning of the dropout probability in large models, and as a result faster experimentation cycles. In RL this allows the agent to adapt its uncertainty dynamically as more data is observed. We analyse the proposed variant extensively on a range of tasks, and give insights into common practice in the field where larger dropout probabilities are often used in deeper model layers

Good transitions : lessons from the ‘Transitions West Midlands’ project

Transitions West Midlands (TWM) is a collaborative project, funded by the Quality Assurance Agency for Higher Education (QAA), that brings together staff and students from a group of institutions (further and higher education) who have been working together for the past four years through the West Midlands Post '92 Research Forum. TWM aims to offer new insights into the first-hand experiences of students making the move or preparing to make the move from Further Education (FE) to Higher Education (HE) within the West Midlands region. The case study approach has enabled us to explore students' expectations of, and reflections on, transition as they move within and between the four participating institutions. The project was driven by three key questions: How do prospective students from under-represented groups in HE understand/perceive their support needs prior to transition? How do HE students from under-represented groups self-define the enablers and barriers to effective transition? How do HE and FE institutions best support students from under-represented groups as they progress through the various stages of transition from FE to HE

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet

Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.Comment: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieva

Numerical and experimental studies of multi-ply woven carbon fibre prepreg forming process

Woven carbon fibre prepreg is being increasingly used in high-performance aerospace and automotive applications, primarily because of its superior mechanical properties and formability. A wide range of forming simulation options are available for predicting material deformation during the prepreg forming process, particularly change in fibre orientation. Development of a robust validated simulation model requires comprehensive material characterisation and reliable experimental validation techniques. This paper presents experimental and numerical methods for studying the fibre orientation in multi-ply woven carbon fibre prepreg forming process, using a double-dome geometry. The numerical study is performed using the commercial forming simulation software PAM-FORM and the material input data are generated from a comprehensive experimental material characterisation. Two experimental validation methods are adopted for fibre shear angle measurement: an optical method for measuring only the surface plies, and a novel CT scan method for measuring both the surface plies and the internal plies. The simulation results are compared against the experimental results in terms of fibre shear angle and the formation of wrinkles to assess the validity of the model

Evaluating a Model of Team Collaboration via Analysis of Team Communications

Human Factors and Ergonomics Society 51st Annual Meeting—2007The article of record may be found at https://doi.org/10.1177/154193120705100456A model of team collaboration was developed that emphasizes the macro-cognitive processes entailed in collaboration and includes major processes that underlie this type of communication: (1) individual knowledge building, (2) developing knowledge inter-operability, (3) team shared understanding, and (4) developing team consensus. This paper describes research conducted to empirically validate this model. Team communications that transpired during two complex problem solving situations were coded using cognitive process definitions included in the model. Data was analyzed for three teams that conducted a Maritime Interdiction Operation (MIO) and four teams that engaged in air-warfare scenarios. MIO scenarios involve a boarding team that boards a suspect ship to search for contraband cargo (e.g. explosives, machinery) and possible terrorist suspects. Air-warfare scenarios involve identifying air contacts in the combat information center of an Aegis ship. The way the teamsí behavior on the two scenarios maps to the model of team collaboration is discussed.Approved for public release; distribution is unlimited

Building Research-Informed Teacher Education Communities: A UCET Framework

©UCET December 201