arXiv:2605.30741v1 Announce Type: new Abstract: Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Several leading methods for UQ linearize DNNs to form Bayesian Generalized Linear Models (GLMs), where epistemic uncertainty is modeled via the predictive posterior distribution. Linearizing around the parameters of the final connected layer of a DNN is a commonly used approximation for reducing the computational burden of such GLMs, though it is often […]

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Diffusion models generate samples through an iterative denoising process, guided by a neural network. While training the denoiser on real-world data is computationally demanding, the sampling procedure itself is more flexible. This adaptability serves as a key lever in practice, enabling improvements in both the quality of generated samples and the efficiency of the sampling process. In this work, we introduce an inverse reinforcement learning framework for learning sampling strategies without retraining the denoiser. We formulate the diffusion […]

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arXiv:2509.21996v2 Announce Type: replace Abstract: Hawkes process models are used in settings where past events increase the likelihood of future events occurring. Many applications record events as counts on a regular grid, yet discrete-time Hawkes models remain comparatively underused and are often constrained by fixed-form baselines and excitation kernels. In particular, there is a lack of flexible, nonparametric treatments of both the baseline and the excitation in discrete time. To this end, we propose the Gaussian Process Discrete […]

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In result of Google’s latest updates to its Maps API services (June 11, 2018), a lot of people are unhappy with these shocking changes. Increase in the price. Decrease in the free map loads. Obligation of adding a billing account. Etc… Therefore, a lot of those people are looking to replace their use of Google Maps to something as good (or at least remotely as good).

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Polycystic Ovary Syndrome (PCOS) is a widespread disorder in women of reproductive age, characterized by a hormonal imbalance, irregular periods, and multiple ovarian cysts. Infertility, metabolic syndrome, and cardiovascular risks are long-term complications that make early detection essential. In this paper, we design a powerful framework based on transfer learning utilizing DenseNet201 and ResNet50 for classifying ovarian ultrasound images. The model was trained on an online dataset containing 3856 ultrasound images of cyst-infected and non-infected patients. Each ultrasound […]

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arXiv:2603.25802v1 Announce Type: new Abstract: Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation learning at the single-cell level remains comparatively underexplored, despite its importance for characterizing cell types and cellular phenotypes. We introduce LEMON (Learning Embeddings from Morphology Of Nuclei), a self-supervised foundation model for scalable single-cell image representation learning. Trained on millions of cell images from […]

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Probabilistic graphical models (PGMs) provide a powerful framework for modelling complex systems, but inference over loopy graphs requires approximate methods whose accuracy depends on how factors are clustered in the graphical representation. Existing factor clustering methods rely on the number of variables in a cluster as a proxy for memory cost and informational content—a loose upper bound that leads to suboptimal merging decisions. We address this limitation by proposing an efficient algorithm for estimating the joint entropy of […]

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arXiv:2603.03484v1 Announce Type: new Abstract: E-fuels are promising long-term energy carriers supporting the net-zero transition. However, the large combinatorial design-operation spaces under renewable uncertainty make the use of mathematical programming impractical for co-optimizing e-fuel production systems. Here, we present MasCOR, a machine-learning-assisted co-optimization framework that learns from global operational trajectories. By encoding system design and renewable trends, a single MasCOR agent generalizes dynamic operation across diverse configurations and scenarios, substantially simplifying design-operation co-optimization under uncertainty. Benchmark comparisons against […]

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