arXiv:2511.07702v1 Announce Type: new Abstract: Multidimensional optimization has consistently been a critical challenge in engineering. However, traditional simulation-based optimization methods have long been plagued by significant limitations: they are typically capable of optimizing only a single problem at a time…
arXiv:2506.08464v2 Announce Type: replace Abstract: Second-order optimization methods for training neural networks, such as KFAC, exhibit superior convergence by utilizing curvature information of loss landscape. However, it comes at the expense of high computational burden. In this work, we analyze…
arXiv:2511.07724v1 Announce Type: new Abstract: The paper addresses the Vehicle Relocation Problem in free-floating car-sharing services by presenting a solution focused on strategies for repositioning vehicles and transferring personnel with the use of scooters. Our method begins by dividing the…
arXiv:2511.07090v2 Announce Type: replace Abstract: Across the Artificial Intelligence (AI) lifecycle – from hardware to development, deployment, and reuse – burdens span energy, carbon, water, and embodied impacts. Cloud provider tools improve transparency but remain heterogeneous and often omit water…
arXiv:2501.05530v2 Announce Type: replace-cross Abstract: This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ…
arXiv:2509.15810v2 Announce Type: replace Abstract: To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-learning to train neural network-based algorithm design policies over a predefined training problem set, which automates the…
arXiv:2511.04807v2 Announce Type: replace Abstract: Given a “data manifold” $Msubset mathbb{R}^n$ and “latent space” $mathbb{R}^ell$, an autoencoder is a pair of continuous maps consisting of an “encoder” $Ecolon mathbb{R}^nto mathbb{R}^ell$ and “decoder” $Dcolon mathbb{R}^ellto mathbb{R}^n$ such that the “round trip”…
arXiv:2410.12982v2 Announce Type: replace Abstract: While transformers have been at the core of most recent advancements in sequence generative models, their computational cost remains quadratic in sequence length. Several subquadratic architectures have been proposed to address this computational issue. Some…
arXiv:2505.22422v2 Announce Type: replace Abstract: The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually all scientific fields. In particular, obtaining the tightest…
arXiv:2511.07470v1 Announce Type: new Abstract: This work demonstrates “slimmable Neural Amp Models”, whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of…
