Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Catastrophic Forgetting"”.
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Referencing Unlabelled World Data to Prevent Catastrophic Forgetting in Class-incremental Learning
… a novel strategy to address the challenge of "catastrophic forgetting" in deep continual-learning systems. The term refers to severe performance degradation for older tasks, as a system learns new tasks that are presented sequentially. Most previous techniques have emphasized preservation of …
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Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
… multi-task learning due to a phenomenon known as catastrophic forgetting. In this thesis, we present a set of simulation environments for exploring the abilities of multi-task drone systems and provide a platform for testing agents in incremental single-agent and multi-agent learning scenarios. …
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Continual Learning for Deep Dense Prediction
… tasks to a new one is known to suffer from the catastrophic forgetting effects. Such forgetting mechanism is problematic as it does not allow us to accumulate knowledge sequentially and requires retaining and retraining on all the training data. Existing techniques for mitigating the abrupt …
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Domain adaptation for neural machine translation
… These include over-fitting, brittleness, and `catastrophic forgetting' of previous training examples. In this thesis we instead explore more robust approaches to domain adaptation for NMT. We consider the case where a system is adapted to a specified domain of interest, but may also need to …
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Non-linguistic Notions in Language Modeling: Learning, Retention, and Applications
… pre-training, comes at a cost for these models: catastrophic forgetting. Thus, learning needs to be followed with retention - making sure these models do not forget what they have learned. Thus, we first motivate the need for numeracy-enhanced models via their potential applications in field of …
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ScPEFT : a parameter-efficient fine-tuning framework for enhancing single-cell large language models in out-of-context
… limited custom data. This approach mitigates catastrophic forgetting, reduces parameter tuning by over 96%, and decreases GPU memory usage by more than half, significantly enhancing scLLMs's accessibility for resourceconstrained researchers. Validated across diverse datasets, scPEFT …
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Disentangled representations in neural models
… problem into subtasks and which experiences no catastrophic forgetting. Together these techniques provide the tools to design a wide range of models that learn disentangled representations and better model the factors of variation in the real world.
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Towards a psychological science of neural network behaviour
… experiments to shed light on the phenomenon of catastrophic forgetting. In particular, through the use of synthetic parameterisable stimuli, we’ll investigate the role of task similarity and task ordering in a continual learning setup. Our analysis leads us to an easy-to-compute heuristic that …
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Efficient Knowledge Transfer and Adaptation for Speech and Beyond
… strategies, we enhance robustness against catastrophic forgetting, a key limitation in continual learning. We also introduce a unique approach to class-incremental audio classification, utilizing mutual information optimization. Second, given the prohibitive computational costs of …
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Continual Learning for Engineering: Benchmarking and Exploring Strategies for 3D Engineering Problems
… learn from sequential data while mitigating catastrophic forgetting, in which there is a loss of performance on previously seen examples. This thesis investigates the application of continual learning to regression-based engineering tasks, with an emphasis on surrogate modeling. We begin by …
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On Deep Network Optimization
… explore optimization avenues such as mitigating catastrophic forgetting in transformers, addressing biases in large language models, and improving transformer classification head design. For each issue, we identify specific problems, propose enhancements, and validate them through extensive …
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Distributed On-line Training for Object Detection on Embedded Devices.
… pare to batch learning, most importantly without forgetting old classes. Our experiment shows that the Distributed Training (using 3-GPUs system) reduces the training time of object detection models by up to 67% comparing with non-distributed system. We test the performance of our Distributed …
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Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design
… We also explored various approaches to mitigate catastrophic forgetting in incremental training of deep learning models.
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Map-Building and Position Estimation in Mobile Robots Using Self-Organizing Maps
… and solutions to the conflicting problem of catastrophic forgetting that these systems are prone to.<br/><br/>The ability to navigate is arguably the most fundamental competence of any mobile agent, besides the ability to avoid basic environmental hazards. Recent studies of insect behavior …
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Incremental Learning in Regression Contexts
… Human Gait Prediction without falling prey to Catastrophic Forgetting. Here, the use case we have in mind is Predictive Exoskeleton Control. Therefore, we evaluate our proposed model on a public, multi modal, Human Gait Database and show, that it delivers competitive results with regard to the …
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On the induction of temporal structure by recurrent neural networks
… SRNs have also been shown to suffer from catastrophic forgetting, lack of syntactic systematicity and an inability to represent more than three levels of centre-embedding, due to the so-called 'vanishing gradients' problem. This problem is caused by the decay of past input information …
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IMPROVING PERFORMANCE OF INTRUSION DETECTION SYSTEMS FOR SOFTWARE-DEFINED NETWORKS
… and lifelong learning methodologies to mitigate catastrophic forgetting and enhance the adaptability of IDSs to zero-day attacks. By leveraging advanced anomaly detection techniques, the framework provides robust protection against previously unseen attack vectors. -Deep Reinforcement Learning …
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Paradigmi ispirati da scienze neurocognitive per il Continual Learning
… o task, tipicamente soffrono del fenomeno del "Catastrophic Forgetting", in quanto l'introduzione di nuove informazioni da acquisire possono sovrascrivere la conoscenza precedentemente acquisita, o addirittura cancellarla. L'essenza del CL consiste nel cercare di contrastare questa limitazione, …
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Advances in Few-Shot Learning for Image Classification and Tabular Data
… in federated learning, and potentially mitigate catastrophic forgetting in adapted systems. In this thesis, we address these challenges by proposing high-performing, parameter-efficient few-shot learning methods for both: i) images, focusing on few-shot classification, i.e. distinguishing …
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From Hidden Data and Information towards Data-Driven Research in the Biomedical Domain
… that arises in such cases is a phenomenon called catastrophic forgetting, which means that the model is biased towards the last training data set and forgets what it has previously learned. <br /><br /> We analyse this effect on biomedical named entity recognition with the current state-of-the-art …
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