Academic
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: …
arXiv:2602.22446v1 Announce Type: new Abstract: Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (GNNs) encounter …
arXiv:2602.22470v1 Announce Type: new Abstract: Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of …
arXiv:2602.22479v1 Announce Type: new Abstract: Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary …
arXiv:2602.22495v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high …
arXiv:2602.22505v1 Announce Type: new Abstract: Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, with masked (absorbing-rate) variants emerging as …
arXiv:2602.22507v1 Announce Type: new Abstract: Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize critical architectural …
arXiv:2602.22520v1 Announce Type: new Abstract: Time series forecasting plays a critical role in domains such as transportation, energy, and meteorology. Despite their success, modern deep …
arXiv:2602.22527v1 Announce Type: new Abstract: Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning …
arXiv:2602.22532v1 Announce Type: new Abstract: Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural …
arXiv:2602.22536v1 Announce Type: new Abstract: Matrix factorization techniques, especially Nonnegative Matrix Factorization (NMF), have been widely used for dimensionality reduction and interpretable data representation. However, …
Since the 2010s, artificial intelligence (AI) has quickly grown from another subset of machine learning (ie deep learning) in particular with recent advances in generative …
