arXiv:2510.17899v1 Announce Type: new Abstract: Automatic performance tuning (auto-tuning) is essential for optimizing high-performance applications, where vast and irregular parameter spaces make manual exploration infeasible. Traditionally, auto-tuning relies on well-established optimization algorithms such as evolutionary algorithms, annealing methods, or surrogate…
arXiv:2510.17895v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into real-world applications, raising concerns about privacy, security and the need to remove undesirable knowledge. Machine Unlearning has emerged as a promising solution, yet faces two key challenges:…
arXiv:2510.17896v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have achieved remarkable success, yet their standard attention mechanism incurs quadratic computation and memory costs with respect to sequence length, posing a major bottleneck for long-context training. Prior work tackles…
arXiv:2510.17890v1 Announce Type: new Abstract: Recent advances in deep learning have led to a surge of open-source models across diverse domains. While model merging offers a promising way to combine their strengths, existing approaches often suffer from parameter conflicts that…
arXiv:2510.17887v1 Announce Type: new Abstract: We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framework integrates three key components, a Fusion DeepONet operator learning architecture…
arXiv:2510.17846v1 Announce Type: new Abstract: Prognostic Health Management (PHM) systems monitor and predict equipment health. A key task is Remaining Useful Life (RUL) estimation, which predicts how long a component, such as a rolling element bearing, will operate before failure.…
arXiv:2510.15946v2 Announce Type: replace Abstract: Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorical devices like irony and metaphor. Existing detection approaches, including MLLM-based techniques, struggle with these…
arXiv:2510.17901v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal FL (HFL), where all participant nodes share the same feature space…
arXiv:2502.01546v2 Announce Type: replace-cross Abstract: Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object’s physical properties and friction with the floor contain significant uncertainties, which makes the task…
arXiv:2510.17914v1 Announce Type: new Abstract: We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable…
