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August 8, 2025A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
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2024Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations. However, a major drawback of most object-centric models, including slot attention
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2024Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts, its exploration in multimodal settings remains underexplored. A recent work has sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces
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CHI 20242024The potential of Generative AI, especially Large Language Models (LLMs), to transform software development is remarkable. In this paper, we focus on one area in software development called “code migration”. We define code migration as the process of transitioning the language version of a code repository by converting both the source code and its dependencies. Carefully designing an effective human-AI partnership
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2024Analytic data systems typically use data layouts to improve the performance of scanning and filtering data. Common data layout techniques include single-column sort keys, compound sort keys, and more complex multidimensional data layouts such as the Z-order. An appropriately-selected data layout over a table, in combination with metadata such as zone maps, enables the system to skip irrele-vant data blocks
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2024In contemporary machine learning approaches to bilingual lexicon induction (BLI), a model learns a mapping between the embedding spaces of a language pair. Recently, the retrieve-and-rank approach to BLI has achieved state-of-the-art results on the task. However, the problem remains challenging in low-resource settings, due to the paucity of data. The task is complicated by factors such as lexical variation
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