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translations. This project involves innovating interesting models to address different aspects of this problem (eg [1,2]). [1] Sequence to Sequence Mixture Model for Diverse Machine Translation Xuanli He
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information during search can be of help not only for solving one problem instance but also in common practical scenarios when a solver needs to be used "incrementally" when dealing with a sequence of
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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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solvers usually run a sequence of algorithms to solve the input instance: first it preprocesses the instance, then it solves its Linear Programming relaxation, runs cutting plane algorithms, primal
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intermediate-mass main-sequence stars using stellar evolution" (with Prof Ilya Mandel) "The impact of new carbon burning rates on the AGB-supernovae mass boundary" (with Prof Alexander Heger) "The connection
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data. Yet, their reliability remains a serious concern. This project aims to first explore current techniques such as fine-tuning, model alignment, prompt engineering and Retrieval Augmented
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Additive exPlanations): Ranks feature importance globally across the patient cohort, validating that age, CA125, HE4, CEA, and systemic inflammatory or protein markers drive predictions in alignment with
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Additive exPlanations): Ranks feature importance globally across the patient cohort, validating that age, CA125, HE4, CEA, and systemic inflammatory or protein markers drive predictions in alignment with
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this problem? This problem has been attacked using multiple different methods for the past 40 years, without much success. But, in 2021, second-year student Cormac Kikkert and I showed that we could