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arXiv:2310.09488v2 Announce Type: replace-cross Abstract: Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual...
arXiv:2403.01673v2 Announce Type: replace-cross Abstract: For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones. To address...
arXiv:2404.02687v3 Announce Type: replace-cross Abstract: A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for...
arXiv:2405.18521v3 Announce Type: replace-cross Abstract: A principal and an agent can launch a project under unanimous consent. Their individual payoffs from the project depend on...
arXiv:2406.05014v4 Announce Type: replace-cross Abstract: The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated....
arXiv:2408.11717v2 Announce Type: replace-cross Abstract: The co-existence of terrestrial and non-terrestrial networks (NTNs) is essential for achieving global coverage in sixth-generation cellular networks. Due to...
arXiv:2411.09686v3 Announce Type: replace-cross Abstract: Regressing a function $F$ on $\mathbb{R}^d$ without the statistical and computational curse of dimensionality requires special statistical models, for example...
arXiv:2502.06323v2 Announce Type: replace-cross Abstract: The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that...
arXiv:2505.17329v3 Announce Type: replace-cross Abstract: A major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is...
arXiv:2505.19447v3 Announce Type: replace-cross Abstract: Self-Supervised Learning (SSL) enables us to pre-train foundation models without costly labeled data. Among SSL methods, Contrastive Learning (CL) methods...
arXiv:2505.21799v4 Announce Type: replace-cross Abstract: The ever-growing scale of deep learning models and training data underscores the critical importance of efficient optimization methods. While preconditioned...
arXiv:2506.00590v2 Announce Type: replace-cross Abstract: A metric relation by definition is symmetric. Since many data sets are non-symmetric, in this paper we develop a systematic...
arXiv:2506.08520v2 Announce Type: replace-cross Abstract: Multi-head self-attention (MHSA) is a key building block in modern vision Transformers, yet its quadratic complexity in the number of...
arXiv:2507.16838v3 Announce Type: replace-cross Abstract: Mispronunciation detection and diagnosis (MDD) is a significant part in modern computer-aided language learning (CALL) systems. Most systems implementing phoneme-level...
arXiv:2508.09020v2 Announce Type: replace-cross Abstract: Understanding the performance of multi-user multiple-input multiple-output (MU-MIMO) systems under imperfect channel state information at the transmitter (CSIT) remains a...
arXiv:2509.03070v3 Announce Type: replace-cross Abstract: This letter presents a locality-aware bearing fault diagnosis framework that operates on time-frequency representations and enables spatially interpretable decision-making. One-dimensional...
arXiv:2510.00771v2 Announce Type: replace-cross Abstract: In this paper, we present a vocoder-free framework for audio super-resolution that employs a flow matching generative model to capture...
arXiv:2510.08916v2 Announce Type: replace-cross Abstract: The representer theorem is a cornerstone of kernel methods, which aim to estimate latent functions in reproducing kernel Hilbert spaces...
arXiv:2510.24710v3 Announce Type: replace-cross Abstract: We study bilevel optimization problems where the lower-level problems are strongly convex and have coupled linear constraints. To overcome the...
arXiv:2510.25753v2 Announce Type: replace-cross Abstract: Pretrained Transformers demonstrate remarkable in-context learning (ICL) capabilities, enabling them to adapt to new tasks from demonstrations without parameter updates....
arXiv:2511.15120v2 Announce Type: replace-cross Abstract: In deep learning, a central issue is to understand how neural networks efficiently learn high-dimensional features. To this end, we...
arXiv:2601.07061v3 Announce Type: replace-cross Abstract: We introduce the setting of continuous index learning, in which a function of many variables varies only along a small...
arXiv:2601.08588v3 Announce Type: replace-cross Abstract: This paper investigates symmetric composite binary quantum hypothesis testing (QHT), where the goal is to determine which of two uncertainty...
arXiv:2601.13818v4 Announce Type: replace-cross Abstract: This paper extends the distributed rolling contact FrBD framework to linear viscoelasticity by considering classic derivative Generalised Maxwell and Kelvin-Voigt...