arXiv:2511.18141v2 Announce Type: replace Abstract: Dirichlet regression models are suitable for compositional data, in which the response variable represents proportions that sum to one. However, there are still no well-established methods for constructing valid prediction sets in this context, especially considering the geometry of the compositional space. In this work, we investigate conformal prediction-based strategies for constructing valid predictive regions in Dirichlet regression models. We evaluate three distinct approaches: a method based on quantile residuals, an approximate construction […]

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I already made a simple neural network and it works, however I struggle with finding a way to make it learn, I just can’t find any information about that, because most of the articles and videos cover only supervised learning which won’t work in my case, or don’t cover backpropagation at all. I just want to see if there are any articles or videos that explain this thoroughly. submitted by /u/KokorakaboboMax [link] [comments]

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arXiv:2601.04219v1 Announce Type: new Abstract: The rapid advancement of large-scale language models (LLMs) has shown their potential to transform intelligent education systems (IESs) through automated teaching and learning support applications. However, current IESs often rely on single-turn static question-answering, which fails to assess learners’ cognitive levels, cannot adjust teaching strategies based on real-time feedback, and is limited to providing simple one-off responses. To address these issues, we introduce AgentTutor, a multi-turn interactive intelligent education system to empower personalized […]

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The transition towards sustainable educational campuses requires robust energy management strategies that integrate operational oversight with advanced analytics. This paper presents a campus-scale electricity monitoring system at the University of Algarve, designed to support the institution’s sustainability goals through continuous monitoring, data reliability, and scalability. The system consolidates heterogeneous meters into a unified platform, enabling precise tracking of energy consumption and photovoltaic generation. Beyond operational efficiency, the platform incorporates a data-driven analytical layer featuring short-term forecasting using Prophet […]

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arXiv:2601.17110v1 Announce Type: new Abstract: Accurate short-term energy consumption forecasting is essential for efficient power grid management, resource allocation, and market stability. Traditional time-series models often fail to capture the complex, non-linear dependencies and external factors affecting energy demand. In this study, we propose a forecasting approach based on Recurrent Neural Networks (RNNs) and their advanced variant, Long Short-Term Memory (LSTM) networks. Our methodology integrates historical energy consumption data with external variables, including temperature, humidity, and time-based features. […]

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arXiv:2602.10056v1 Announce Type: cross Abstract: We introduce WildCat, a high-accuracy, low-cost approach to compressing the attention mechanism in neural networks. While attention is a staple of modern network architectures, it is also notoriously expensive to deploy due to resource requirements that scale quadratically with the input sequence length $n$. WildCat avoids these quadratic costs by only attending over a small weighted coreset. Crucially, we select the coreset using a fast but spectrally-accurate subsampling algorithm — randomly pivoted Cholesky […]

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Digital Trust Is Not a Feature – It’s a Philosophical Problem For most of human history, trust was something we negotiated face to face. We trusted people we knew, institutions we could see, and systems that evolved slowly enough for social norms to keep pace. Today, that foundation is gone. Trust now flows through code, algorithms, platforms, and automated decisions – often invisible, often opaque, and rarely negotiable. This is not merely a technological shift. It is ==a […]

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