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arXiv:2603.20616v1 Announce Type: new Abstract: Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting …
arXiv:2603.20632v1 Announce Type: new Abstract: Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. …
arXiv:2603.20634v1 Announce Type: new Abstract: Accurately characterizing non-linear functional manifolds with singularities is a fundamental challenge in scientific computing. While Multi-Layer Perceptrons (MLPs) dominate, their …
arXiv:2603.20645v1 Announce Type: new Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated …
arXiv:2603.20655v1 Announce Type: new Abstract: We introduce Exponential Family Discriminant Analysis (EFDA), a unified generative framework that extends classical Linear Discriminant Analysis (LDA) beyond the …
arXiv:2603.20671v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action …
arXiv:2603.20684v1 Announce Type: new Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the …
arXiv:2603.20687v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing, yet their performance is often constrained by the limited …
arXiv:2603.20775v1 Announce Type: new Abstract: In personalized marketing, uplift models estimate incremental effects by modeling how customer behavior changes under alternative treatments. However, real-world data …
arXiv:2603.20777v1 Announce Type: new Abstract: Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target …
arXiv:2603.20791v1 Announce Type: new Abstract: Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial …
arXiv:2603.20801v1 Announce Type: new Abstract: Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively …