Risk-management with Bayesian deep learning

Characterisation Virtual Laboratory

Participants of this workshop will be provided with instruction and code to generate estimates of uncertainty in deep learning.

JCU, Townsville Campus,  Earth and Environmental Sciences Building

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We regret to inform you that this event has been postponed until further notice. ----------------------------The Characterisation Commons continues ongoing efforts to train researchers in imaging processing techniques. This time we are reaching the audience at JCU with the topic of Bayesian Deep learning and risk-management. JCU is partnering with the The Characterisation Virtual Laboratory project to provide this workshop to researches involved in image analysis and processing. Workshop descriptionParticipants of this workshop will be provided with the instruction and code to generate estimates of uncertainty in deep learning. Practical examples will demonstrate how uncertainties can be used to improve the interpretability of image analysis. Simple risk-management techniques will be shared to improve both the predictive performance and robustness against outlier data. A discussion will be made about how we can simply overcome &quot;catastrophic silent failure&quot; by out-of-distribution examples. This workshop will be kept high-level with all code provided for. Pre-requisitesBe motivated to use deep learning techniques to your research in the future and share your learning outcomes. Basic knowledge of Python and statistics will help, but is not absolutely necessary.Software and ComputeThis is a bring your own laptop workshop. We will opt for two optional setups. 1. You will need to install all the software required in your own laptop. 2. You will need to get an account on the CVL @ MASSIVE to use to get access to a virtual computer. Both options will follow with a set of instructions, which you can select during registrations. You must prepare for the setup a few days prior to the workshop.InstructorSamual MacDonald from Max Kelsen.What is the CVL?The Characterisation Virtual Laboratory is a free cloud-based virtual desktop workbench to perform analysis of complex image and microscopy data. It serves to run analyses in a large computing infrastructure, all embedded in a web browser accessible using AAF (Australian Access Federation), and connected to a HPC (high performance computing) infrastructure. For more information please visit <a href="https://www.cvl.org.au/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">cvl.org.au</a>.What software tools are available?<a href="https://docs.massive.org.au/M3/software/software.html" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">About 800 different versions of tools for image analysis and visualisation</a> are available through the CVL. A Virtual Laboratory, such as the CVL can save researchers time as they do not have to create and maintain their own online environments and tools.Virtual DesktopCurrently the virtual desktop service is provided by MASSIVE hosted by Monash University as a national resource. To get started with the CVL at MASSIVE please follow the instructions <a href="https://docs.massive.org.au/M3/requesting-an-account.html" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">here</a>.Contact for General enquiries about the CVL@MASSIVE email <a href="http://help@massive.org.au" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">MASSIVE helpdesk</a>

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Risk-management with Bayesian deep learning

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Risk-management with Bayesian deep learning

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