Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of n ....Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of nearly pure additions to fulfil the requisites of accuracy, robustness, calibration and generalisation in real-world computer vision tasks. The success of this project will benefit deep learning-based products on smartphones or robots in health and cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101591
Funder
Australian Research Council
Funding Amount
$419,154.00
Summary
Towards Real-world Continual Learning on Unrestricted Task Steams. This project aims to enable machines to continually learn without forgetting and accumulate knowledge from the sequential data streams containing diverse tasks. This project expects to advance the continual learning to unrestricted real-world task steams that are long-term and complex and promote artificial intelligence toward the human-level intelligence that can automatically evolve during interaction with the world. Expected o ....Towards Real-world Continual Learning on Unrestricted Task Steams. This project aims to enable machines to continually learn without forgetting and accumulate knowledge from the sequential data streams containing diverse tasks. This project expects to advance the continual learning to unrestricted real-world task steams that are long-term and complex and promote artificial intelligence toward the human-level intelligence that can automatically evolve during interaction with the world. Expected outcomes of this project include the paradigm-shifting continual learning framework and techniques for handling unrestricted task steams in real-world scenarios. They will benefit society and the economy nationally and internationally by enhancing the applicability of artificial intelligence.Read moreRead less
Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training ....Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training, which are anticipated to deepen our understanding of visual representation and make it easier to build and deploy computer vision applications and services. Examples of benefits include modernising machines in manufacturing and farming with visual intelligence. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100967
Funder
Australian Research Council
Funding Amount
$366,000.00
Summary
Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected o ....Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected outcomes include new paradigms for algorithm design and evaluation, and establishing the problem as a focus of international research. The key practical benefit would be to accelerate the wider deployment of visual perception in applications such as autonomous vehicles, interactive robotics, and video analysis.Read moreRead less
Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili ....Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.Read moreRead less
EEG Based Global Network Models and Platform for Brain States Assessment. This project aims to generate new knowledge and tools in global brain network modelling and deep learning technology. It addresses the significant issues in higher brain function state assessment using brain signal EEG. The project applies global brain networks to model brain dynamical activities as a whole, and assesses higher brain functions such as consciousness, fatigue, sleep, stress and depression, and their step by ....EEG Based Global Network Models and Platform for Brain States Assessment. This project aims to generate new knowledge and tools in global brain network modelling and deep learning technology. It addresses the significant issues in higher brain function state assessment using brain signal EEG. The project applies global brain networks to model brain dynamical activities as a whole, and assesses higher brain functions such as consciousness, fatigue, sleep, stress and depression, and their step by step evolution in real-time using innovative deep learning approaches. The expected outcomes are optimised brain network models and a platform technology. The intended results can be applied to greatly improve the sleep quality and productivity of general community, and the safety of workplace and transportation.Read moreRead less
Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integra ....Using cognitive models to understand memorability of real world images. This proposal aims to understand and make predictions about which real world images -- specifically living things, objects, and human faces -- that people will remember remember via an integration of cognitive models of memory and machine learning techniques. Computer vision models and similarity scaling techniques will be used to produce psychological representations of the images. These representations will then be integrated with cognitive models of memory, which predict that images are more likely to be recognized if they are similar to each of the representations in memory. Large scale memory and similarity rating datasets will be used to develop and test the model.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100144
Funder
Australian Research Council
Funding Amount
$444,447.00
Summary
Universal Model Selection Criteria for Scientific Machine Learning. This project aims to develop provably reliable universal model selection criteria to facilitate trustworthy scientific machine learning. Combining stochastic methods with an innovative geometric approach to basic statistical principles, this project expects to characterise, combine, and refine the most successful heuristics for designing and training huge models, such as deep neural networks, into a cohesive theoretical framewor ....Universal Model Selection Criteria for Scientific Machine Learning. This project aims to develop provably reliable universal model selection criteria to facilitate trustworthy scientific machine learning. Combining stochastic methods with an innovative geometric approach to basic statistical principles, this project expects to characterise, combine, and refine the most successful heuristics for designing and training huge models, such as deep neural networks, into a cohesive theoretical framework. The expected outcomes include a general toolkit for assisting neural network design at the forefront of scientific applications. This should significantly improve the quality of scientific predictions by facilitating confident adoption of deep learning methods into the pantheon of trustworthy modeling techniques. Read moreRead less