arXiv:2511.07658v1 Announce Type: new Abstract: Although recent advancements in learning-based analog circuit design automation have tackled tasks such as topology generation, device sizing, and layout synthesis, efficient performance evaluation remains a major bottleneck. Traditional SPICE simulations are time-consuming, while existing…
arXiv:2511.08577v1 Announce Type: cross Abstract: Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation…
arXiv:2511.07694v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong performance across various natural language processing (NLP) tasks but remain vulnerable to hallucinations, generating factually incorrect or misleading outputs. Uncertainty estimation, often using predictive entropy estimation, is key to…
arXiv:2406.01539v3 Announce Type: replace Abstract: On the forefront of scientific computing, Deep Learning (DL), i.e., machine learning with Deep Neural Networks (DNNs), has emerged a powerful new tool for solving Partial Differential Equations (PDEs). It has been observed that DNNs…
arXiv:2511.07700v1 Announce Type: new Abstract: Artificial Intelligence (AI) models have demonstrated expert-level performance in melanoma detection, yet their clinical adoption is hindered by performance disparities across demographic subgroups such as gender, race, and age. Previous efforts to benchmark the performance…
arXiv:2501.06268v2 Announce Type: replace Abstract: We introduce a new method for clustering based on Cluster Catch Digraphs (CCDs). The new method addresses the limitations of RK-CCDs by employing a new variant of spatial randomness test that employs the nearest neighbor…
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…
