arXiv:2511.16778v1 Announce Type: new Abstract: Recently, structure-text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks and language models. However, existing methods typically rely on homophily assumptions in similarity estimation and…
arXiv:2511.16767v1 Announce Type: new Abstract: Graphs provide a unified representation of semantic content and relational structure, making them a natural fit for domains such as molecular modeling, citation networks, and social graphs. Meanwhile, large language models (LLMs) have excelled at…
arXiv:2511.02969v2 Announce Type: replace Abstract: Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $epsilon$-greedy and Boltzmann…
arXiv:2511.16828v1 Announce Type: new Abstract: Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model…
arXiv:2207.07250v3 Announce Type: replace-cross Abstract: We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of…
arXiv:2511.16839v1 Announce Type: new Abstract: Transformers have defined the state-of-the-art for clinical prediction tasks involving electronic health records (EHRs). The recently introduced Mamba architecture outperformed an advanced Transformer (Transformer++) based on Llama in handling long context lengths, while using fewer…
arXiv:2508.13663v2 Announce Type: replace-cross Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are emph{likely} to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However,…
arXiv:2511.16845v1 Announce Type: new Abstract: Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction (CP) is a general UQ framework that provides…
arXiv:2511.17380v1 Announce Type: cross Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motivating the recent proposal of probabilistic robustness (PR) as a complementary alternative to adversarial robustness (AR).…
arXiv:2511.16848v1 Announce Type: new Abstract: Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats,…
