arXiv:2511.00040v1 Announce Type: new Abstract: The field of preference optimization has made outstanding contributions to the alignment of language models with human preferences. Despite these advancements, recent methods still rely heavily on substantial paired (labeled) feedback data, leading to substantial…
arXiv:2511.00043v1 Announce Type: new Abstract: In this study, we present and validate the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dynamical systems governed by ordinary differential equations (ODEs). While traditional…
arXiv:2511.00035v1 Announce Type: new Abstract: The dynamic energy sector requires both predictive accuracy and runtime efficiency for short-term forecasting of energy generation under operational constraints, where timely and precise predictions are crucial. The manual configuration of complex methods, which can…
arXiv:2511.00032v1 Announce Type: new Abstract: In recent years, Neural Operators(NO) have gradually emerged as a popular approach for solving Partial Differential Equations (PDEs). However, their application to large-scale engineering tasks suffers from significant computational overhead. And the fact that current…
arXiv:2511.00030v1 Announce Type: new Abstract: Machine unlearning has emerged as a prevalent technical solution for selectively removing unwanted knowledge absorbed during pre-training, without requiring full retraining. While recent unlearning techniques can effectively remove undesirable content without severely compromising performance on…
arXiv:2508.14368v2 Announce Type: replace-cross Abstract: I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation…
The new approach maps aircraft sections most vulnerable to lightning, including on planes with experimental designs.
arXiv:2410.11067v3 Announce Type: replace-cross Abstract: Given an intractable target density $p$, variational inference (VI) attempts to find the best approximation $q$ from a tractable family $Q$. This is typically done by minimizing the exclusive Kullback-Leibler divergence, $text{KL}(q||p)$. In practice, $Q$…
arXiv:2505.13136v2 Announce Type: replace-cross Abstract: Encoders remain essential for efficient German NLP and NLU scenarios despite the rise of decoder-only LLMs. This work studies two routes to high-quality German encoders under identical data and training constraints: 1) training from scratch…
arXiv:2507.22959v2 Announce Type: replace Abstract: The field of scientific machine learning, which originally utilized multilayer perceptrons (MLPs), is increasingly adopting Kolmogorov-Arnold Networks (KANs) for data encoding. This shift is driven by the limitations of MLPs, including poor interpretability, fixed activation…
