arXiv:2508.00643v2 Announce Type: replace Abstract: Operator learning is a powerful paradigm for solving partial differential equations, with Fourier Neural Operators serving as a widely adopted foundation. However, FNOs face significant scalability challenges due to overparameterization and offer no native uncertainty…
arXiv:2503.21473v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their $O(N^3)$ scaling makes them intractable for large datasets. Approximate methods such as variational inference (VI), inducing points (sparse GPs),…
arXiv:2510.12328v3 Announce Type: replace Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (GNNs) combined with extreme-value analysis techniques to improve gauge-station rainfall…
arXiv:2504.00010v3 Announce Type: replace Abstract: Text-to-image (T2I) generation has made remarkable progress, yet existing systems still lack intuitive control over spatial composition, object consistency, and multi-step editing. We present $textbf{LayerCraft}$, a modular framework that uses large language models (LLMs) as…
arXiv:2507.02998v2 Announce Type: replace Abstract: Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to their low prevalence and limited clinician familiarity. Computational phenotyping offers a scalable approach to improving rare…
arXiv:2510.15669v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) are a leading approach to address the problem of learning disentangled representations. Typically a single VAE is used and disentangled representations are sought in its continuous latent space. Here we explore a…
arXiv:2411.08590v4 Announce Type: replace Abstract: Associative memory models, such as Hopfield networks and their modern variants, have garnered renewed interest due to advancements in memory capacity and connections with self-attention in transformers. In this work, we introduce a unified framework-Hopfield-Fenchel-Young…
arXiv:2510.15056v1 Announce Type: new Abstract: Reinforcement learning usually assumes a given or sometimes even fixed environment in which an agent seeks an optimal policy to maximize its long-term discounted reward. In contrast, we consider agents that are not limited to…
arXiv:2510.15061v1 Announce Type: new Abstract: Widespread LLM adoption has introduced characteristic repetitive phraseology, termed “slop,” which degrades output quality and makes AI-generated text immediately recognizable. We present Antislop, a comprehensive framework providing tools to both detect and eliminate these overused…
arXiv:2510.15044v1 Announce Type: new Abstract: Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals’ access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its…
