arXiv:2512.19722v1 Announce Type: new Abstract: This work introduces a methodology to adjust forecasts based on node-specific cost function asymmetry. The proposed model generates savings by dynamically incorporating the cost asymmetry into the forecasting error probability distribution to favor the least…
arXiv:2512.19719v1 Announce Type: new Abstract: Targeted maintenance strategies, ensuring the dependability and safety of industrial machinery. However, current modeling techniques for assessing both local and global correlation of battery degradation sequences are inefficient and difficult to meet the needs in…
arXiv:2512.19720v1 Announce Type: new Abstract: Serving many task-specialized LLM variants is often limited by the large size of fine-tuned checkpoints and the resulting cold-start latency. Since fine-tuned weights differ from their base model by relatively small structured residuals, a natural…
arXiv:2512.19718v1 Announce Type: new Abstract: In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across heterogeneous…
arXiv:2512.19717v1 Announce Type: new Abstract: Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. We present a practical framework, emph{Inverted Causality Focusing Algorithm} (ICFA), that treats search…
arXiv:2512.19716v1 Announce Type: new Abstract: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mortality…
arXiv:2412.06867v2 Announce Type: replace Abstract: Low-rank factorization is a popular model compression technique that minimizes the error $delta$ between approximated and original weight matrices. Despite achieving performances close to the original models when $delta$ is optimized, a performance discrepancy remains…
arXiv:2512.19723v1 Announce Type: new Abstract: This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environments. While existing approaches treat data quality assessment and ML systems as isolated…
arXiv:2505.15015v2 Announce Type: replace Abstract: Most Graph Neural Networks (GNNs) propagate messages by treating node embeddings as holistic feature vectors, implicitly assuming uniform relevance across feature dimensions. This limits their ability to selectively transmit informative components, especially when graph structures…
arXiv:2512.19725v1 Announce Type: new Abstract: Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields, fueled by increased computational resources, large-scale datasets, and the rise of deep learning architectures. From malware…
