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Showing results 41-50 of 691.

ISTD PhD Oral Defence Seminar by Duo Peng – Multilevel diffusion-based domain adaptation: image, pixel, and category

ISTD PhD Oral Defence Seminar by Duo Peng – In this paper, we investigate Diffusion-Based Domain Adaptation, leveraging emerging
diffusion models to address domain adaptation tasks. The motivation behind our research stems from the powerful distribution transformation capabilities of diffusion models, which we aim to harness to help AI models adapt to new data distributions.

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ISTD PhD Oral Defence Seminar by Dai Siyang – Urban intelligence: machine learning for human and environmental insights

ISTD PhD Oral Defence Seminar by Dai Siyang – Urbanisation has given rise to increasingly complex systems that sustain and manage the lives of urban residents. As populations grow, the challenges of managing traffic, overcrowding, and environmental challenges escalate. These complexities place stress on urban ecosystems, demanding innovative solutions to ensure smooth and sustainable operations.

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ISTD PhD Oral Defence Seminar by Chia Yew Ken – Extracting and reasoning with structured information in natural language and beyond

ISTD PhD Oral Defence Seminar by Chia Yew Ken – This thesis investigates the crucial role of structured information in natural language processing and artificial intelligence, with a focus on its extraction, utilisation, and extension to multimodal reasoning.

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ISTD PhD Oral Defence Seminar by Bhardwaj Rishabh – AI metrics beyond performance: safety and trustworthiness of AI systems

ISTD PhD Oral Defence Seminar by Bhardwaj Rishabh – This thesis investigates critical non-idealities in AI systems, focusing on safety behaviour post-training and alignment.

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ISTD PhD Oral Defence Seminar by Joel Ong – Modern portfolio construction with advanced deep learning models

ISTD PhD Oral Defence Seminar by Joel Ong – We explore the modern application of deep learning techniques in portfolio construction, presenting innovative methodologies that significantly enhance traditional investment strategies. Central to this research are three advanced frameworks that leverage deep learning to optimize financial portfolios.

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Congratulations to Professor Zhou Jianying and his visiting PhD Student for receiving the Distinguished Paper Award at ACSAC 2023

Congratulations to Professor Zhou Jianying and his visiting PhD Student for receiving the Distinguished Paper Award at ACSAC 2023

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Congratulations to Associate Professor Liu Xiaogang’s PhD Student in Winning the Merit Award in the "Visual Science" Cover Contest

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in Winning the Merit Award in the “Visual Science” Cover Contest

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ISTD PhD Oral Defence Seminar by Hong Pengfei – Beyond benchmarks: measuring and strengthening generalisable reasoning in large language models

ISTD PhD Oral Defence Seminar by Hong Pengfei – This thesis addresses critical questions surrounding the evaluation and enhancement of reasoning robustness, generalisability, and comprehensiveness in modern language models, particularly under realistic conditions involving noise, ambiguity, domain shifts, and multimodal inputs.

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ISTD PhD Oral Defence Seminar by Ho Ngai Lam – Utilising large language models for tour itinerary recommendation

ISTD PhD Oral Defence Seminar by Ho Ngai Lam – Planning a tour Itinerary poses a significant challenge for tourists, especially when navigating unfamiliar territories. The computational complexity of tour recommendation further compounds this challenge due to its inherent intricacies.

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ISTD PhD Oral Defence Seminar by Zeng Guangtao – Beyond scale: efficient pre-training and controllable post-training for language models

ISTD PhD Oral Defence Seminar by Zeng Guangtao – Language models are foundational to modern artificial intelligence, but their development is often constrained by challenges in efficiency, controllability, and reasoning. In this thesis, we aim to address these limitations by introducing advanced paradigms at both the pre-training and post-training stages.

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