arXiv:2506.08849v4 Announce Type: replace
Abstract: Vision-Language Foundation Models (VLFMs) exhibit remarkable generalization, yet their direct application to medical ultrasound is severely hindered by a profound modality gap. The unique acoustic ph…
arXiv:2605.01817v1 Announce Type: new
Abstract: Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the deliberate absence of a signal. As a result, they …
arXiv:2605.01468v1 Announce Type: cross
Abstract: Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this problem by generating additional data, while head-to-…
Over the years, enterprise IT execs have gotten frighteningly comfortable having little control or visibility over mission-critical apps, from SaaS to cloud and even cybersecurity. But generative AI (genAI) and agentic systems ar…
arXiv:2505.13007v2 Announce Type: replace
Abstract: The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous material properties…
arXiv:2605.00716v1 Announce Type: new
Abstract: Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insigh…
arXiv:2604.26181v2 Announce Type: replace
Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Curre…
arXiv:2605.00169v1 Announce Type: cross
Abstract: Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces signi…
arXiv:2507.09001v3 Announce Type: replace-cross
Abstract: Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham density functional theory calculations, as it is not know…
arXiv:2605.00324v1 Announce Type: cross
Abstract: Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining — resulting in long iteration cycles (3–6 mon…