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arXiv:2602.12338v1 Announce Type: new Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications …
arXiv:2602.12368v1 Announce Type: new Abstract: This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on $S^2$ …
arXiv:2602.12379v1 Announce Type: new Abstract: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback. While Iterative Conditional Expectation …
arXiv:2602.12391v1 Announce Type: new Abstract: Level set estimation (LSE) classifies whether an unknown function's value exceeds a specified threshold for given inputs, a fundamental problem …
arXiv:2602.12394v1 Announce Type: new Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily …
arXiv:2602.12429v1 Announce Type: new Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges. Low-rank factorization offers …
arXiv:2602.12449v1 Announce Type: new Abstract: Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, …
arXiv:2602.12468v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous …
arXiv:2602.12469v1 Announce Type: new Abstract: Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that inflates …
arXiv:2602.12471v1 Announce Type: new Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary …
arXiv:2602.12482v1 Announce Type: new Abstract: We give a geometric construction of neural networks that separate disjoint compact subsets of $\Bbb R^n$, and use it to …
arXiv:2602.12499v1 Announce Type: new Abstract: The recent empirical success of Mamba and other selective state space models (SSMs) has renewed interest in non-attention architectures for …