arXiv:2509.04734v2 Announce Type: replace Abstract: The Information Contrastive (I-Con) framework revealed that over 23 representation learning methods implicitly minimize KL divergence between data and learned distributions that encode similarities between data points. However, a KL-based loss may be misaligned with…
arXiv:2512.04354v1 Announce Type: new Abstract: Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts…
arXiv:2510.25121v2 Announce Type: replace Abstract: Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial…
arXiv:2512.04385v1 Announce Type: new Abstract: Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and buses offers a low-cost, easy-to-maintain, and wide-coverage data collection solution.…
arXiv:2511.15371v2 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model’s decision-making process. While numerous methods exist, the lack of a definitive ground truth for comparison highlights the need for alternative,…
arXiv:2512.04388v1 Announce Type: new Abstract: Powerful large language models (LLMs) from different providers have been expensively trained and finetuned to specialize across varying domains. In this work, we introduce a new kind of Conductor model trained with reinforcement learning to…
arXiv:2512.01152v2 Announce Type: replace Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. Such shifts can take many forms: new classes may…
arXiv:2512.04464v1 Announce Type: new Abstract: We challenge the common belief that deep learning always trumps older techniques, using the example of grading Saint-Gaudens Double Eagle gold coins automatically. In our work, we put a feature-based Artificial Neural Network built around…
arXiv:2503.09321v2 Announce Type: replace-cross Abstract: Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-modal learning, existing benchmarks often suffer from strong visual bias —…
arXiv:2512.04475v1 Announce Type: new Abstract: Machine learning on graphs has recently achieved impressive progress in various domains, including molecular property prediction and chip design. However, benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, which…
