arXiv:2502.18300v4 Announce Type: replace Abstract: Bayesian methods have shown success in deep learning applications. For example, in predictive tasks, Bayesian neural networks leverage Bayesian reasoning of model uncertainty to improve the reliability and uncertainty awareness of deep neural networks. In…
arXiv:2510.16983v1 Announce Type: cross Abstract: Diffusion and flow models achieve high generative quality but remain computationally expensive due to slow multi-step sampling. Distillation methods accelerate them by training fast student generators, yet most existing objectives lack a unified theoretical foundation.…
arXiv:2510.17512v1 Announce Type: cross Abstract: Prevailing practice in learning-based audio watermarking is to pursue robustness by expanding the set of simulated distortions during training. However, such surrogates are narrow and prone to overfitting. This paper presents AWARE (Audio Watermarking with…
arXiv:2510.15955v1 Announce Type: new Abstract: Most realistic task automation problems require large language models (LLMs) to call tools, which often return complex JSON responses. These responses must be further processed to derive the information necessary for task completion. The ability…
arXiv:2506.20048v2 Announce Type: replace-cross Abstract: In reinforcement learning, distributional off-policy evaluation (OPE) focuses on estimating the return distribution of a target policy using offline data collected under a different policy. This work focuses on extending the widely used fitted Q-evaluation…
arXiv:2510.15950v1 Announce Type: new Abstract: Parkinson’s disease (PD) presents a growing global challenge, affecting over 10 million individuals, with prevalence expected to double by 2040. Early diagnosis remains difficult due to the late emergence of motor symptoms and limitations of…
arXiv:2510.15954v1 Announce Type: new Abstract: As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation plays a vital role by…
arXiv:2510.15947v1 Announce Type: new Abstract: This study introduces a WaveNet-based deep learning model designed to automate the classification of EEG signals into physiological, pathological, artifact, and noise categories. Traditional methods for EEG signal classification, which rely on expert visual review,…
arXiv:2510.15946v1 Announce Type: new 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:2510.15945v1 Announce Type: new Abstract: Sampling multiple responses is a common way to improve LLM output quality, but it comes at the cost of additional computation. The key challenge is deciding when to stop generating new samples to balance accuracy…
