arXiv:2608. 15362v1 Announce Type: cross Abstract: We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty.
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arXiv:2607. 00470v1 Announce Type: cross Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time.
We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon.
The paper introduces the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns a nonlinear, vector-valued residual representation from recent prediction errors. VARNN conditions subsequent predictions on this learned residual-memory state, mapping scalar prediction innovations into a short-context representation. Experiments on nine datasets across energy, healthcare, and environmental domains show that VARNN achieves lower test MSE than static, lag-based, and sequence-model baselines, and ablations confirm that the learned residual memory improves predictive accuracy over direct scalar residual feedback.
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.
arXiv:2605. 00600v2 Announce Type: replace-cross Abstract: Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling.