arXiv:2509.13520v1 Announce Type: new Abstract: Neural surrogates and operator networks for solving partial differential equation (PDE) problems have attracted significant research interest in recent years. However, most existing approaches are limited in their ability to generalize solutions across varying non-parametric…
arXiv:2407.06544v2 Announce Type: replace Abstract: We explore multiple instance verification, a problem setting in which a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. We show that naive adaptations of attention-based multiple instance learning…
arXiv:2509.13634v1 Announce Type: new Abstract: Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies,…
arXiv:2502.12484v2 Announce Type: replace Abstract: Neural solvers have shown significant potential in solving the Traveling Salesman Problem (TSP), yet current approaches face significant challenges. Supervised learning (SL)-based solvers require large amounts of high-quality labeled data, while reinforcement learning (RL)-based solvers,…
arXiv:2509.13636v1 Announce Type: new Abstract: This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image matrices to enhance stress detection using convolutional neural networks (CNNs). Unlike traditional approaches…
arXiv:2509.05037v3 Announce Type: replace Abstract: Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present ModaliSurv, a multimodal deep survival model utilising DeepHit with a…
arXiv:2509.13642v1 Announce Type: new Abstract: We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the “one-tool” bottleneck of current unified models, which are limited to synthetic…
arXiv:2408.17432v3 Announce Type: replace-cross Abstract: Synthesizing the voices of unseen speakers remains a persisting challenge in multi-speaker text-to-speech (TTS). Existing methods model speaker characteristics through speaker conditioning during training, leading to increased model complexity and limiting reproducibility and accessibility. A…
arXiv:2509.13648v1 Announce Type: new Abstract: Generative recommendation plays a crucial role in personalized systems, predicting users’ future interactions from their historical behavior sequences. A critical yet underexplored factor in training these models is data augmentation, the process of constructing training…
arXiv:2505.18614v3 Announce Type: replace-cross Abstract: Lyrics translation requires both accurate semantic transfer and preservation of musical rhythm, syllabic structure, and poetic style. In animated musicals, the challenge intensifies due to alignment with visual and auditory cues. We introduce Multilingual Audio-Video…
