arXiv:2505.18636v2 Announce Type: replace Abstract: The go-to strategy to apply deep networks in settings where uncertainty informs decisions–ensembling multiple training runs with random initializations–is ill-suited for the extremely large-scale models and practical fine-tuning workflows of today. We introduce a new…
arXiv:2511.20826v1 Announce Type: new Abstract: Untrained large neural networks, just after random initialization, tend to favour a small subset of classes, assigning high predicted probabilities to these few classes and approximately zero probability to all others. This bias, termed Initial…
arXiv:2511.20830v1 Announce Type: new Abstract: The solar wind, a continuous outflow of charged particles from the Sun’s corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is…
arXiv:2511.10234v2 Announce Type: replace Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operating on sequential graph serializations, LLMs can produce different outputs under node reindexing, edge reordering, or formatting…
arXiv:2511.20839v1 Announce Type: new Abstract: We present Primal, a deterministic feature mapping framework that harnesses the number-theoretic independence of prime square roots to construct robust, tunable vector representations. Diverging from standard stochastic projections (e.g., Random Fourier Features), our method exploits…
arXiv:2511.20099v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural language descriptions that are often ambiguous, redundant, and unstructured, which poses significant challenges…
arXiv:2511.20844v1 Announce Type: new Abstract: Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approaches for learning with noisy labels often rely on the availability of a clean subset…
arXiv:2502.17690v3 Announce Type: replace-cross Abstract: Inferring stochastic dynamics from data is central across the sciences, yet in many applications only unordered, non-sequential measurements are available-often restricted to limited regions of state space-so standard time-series methods do not apply. We introduce…
arXiv:2511.20870v1 Announce Type: new Abstract: State resetting is a fundamental but often overlooked capability of simulators. It supports sample-based planning by allowing resets to previously encountered simulation states, and enables calibration of simulators using real data by resetting to states…
arXiv:2507.11137v2 Announce Type: replace-cross Abstract: As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they…
