arXiv:2508.18948v2 Announce Type: replace-cross Abstract: We develop a gauge-covariant stochastic effective field theory for stability and finite-width effects in deep neural systems. The model uses classical commuting fields: a complex matter field, a real Abelian connection field, and a fictitious…
arXiv:2603.17478v2 Announce Type: replace Abstract: This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamforming and waveforms. We convert the iterative proximal gradient descent (PGD) algorithm into a deep neural network,…
arXiv:2411.16719v4 Announce Type: replace-cross Abstract: Domain randomization through synthesis is a powerful strategy to train networks that are unbiased with respect to the domain of the input images. Randomization allows networks to see a virtually infinite range of intensities and…
arXiv:2508.01575v2 Announce Type: replace Abstract: Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains challenging. Existing LTSF approaches, dominated by MLP- and Transformer-based architectures, either rely on simple linear…
arXiv:2512.12325v3 Announce Type: replace Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural “Ville event” $mathcal E_alpha$, this regret till time…
arXiv:2604.20595v1 Announce Type: cross Abstract: We establish a mathematical correspondence between state space models, a state-of-the-art architecture for capturing long-range dependencies in data, and an exactly solvable nonlinear oscillator network. As a specific example of this general correspondence, we analyze…
arXiv:2407.11933v4 Announce Type: replace Abstract: Target-group detection is the task of detecting which group(s) a piece of content is “directed at or about”. Applications include targeted marketing, content recommendation, and group-specific content assessment. Key challenges include: 1) that a single…
arXiv:2604.19859v1 Announce Type: new Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we study how to train a strong small deep…
arXiv:2604.19877v1 Announce Type: new Abstract: We release Super Apriel, a 15B-parameter supernet in which every decoder layer provides four trained mixer choices — Full Attention (FA), Sliding Window Attention (SWA), Kimi Delta Attention (KDA), and Gated DeltaNet (GDN). A placement…
arXiv:2604.19840v1 Announce Type: new Abstract: Graph-theoretic approaches offer simplicity, interpretability, and low computational cost for molecular property prediction. Among these, the model proposed by Mukwembi and Nyabadza, based on the external activity $D(G)$ and internal activity $zeta(G)$ indices, achieved strong…
