arXiv:2407.17032v4 Announce Type: replace Abstract: Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment…
arXiv:2505.00162v2 Announce Type: replace Abstract: Efficient optimization remains a fundamental challenge across numerous scientific and engineering domains, especially when objective function and gradient evaluations are computationally expensive. While zeroth-order optimization methods offer effective approaches when gradients are inaccessible, their practical…
arXiv:2511.00044v1 Announce Type: new Abstract: Physical Neural Networks (PNN) are promising platforms for next-generation computing systems. However, recent advances in digital neural network performance are largely driven by the rapid growth in the number of trainable parameters and, so far,…
arXiv:2511.01628v1 Announce Type: cross Abstract: Bayesian neural networks (BNNs) allow rigorous uncertainty quantification in deep learning, but often come at a prohibitive computational cost. We propose three different innovative architectures of partial trace-class Bayesian neural networks (PaTraC BNNs) that enable…
The new approach maps aircraft sections most vulnerable to lightning, including on planes with experimental designs.
arXiv:2410.11067v3 Announce Type: replace-cross Abstract: Given an intractable target density $p$, variational inference (VI) attempts to find the best approximation $q$ from a tractable family $Q$. This is typically done by minimizing the exclusive Kullback-Leibler divergence, $text{KL}(q||p)$. In practice, $Q$…
The new approach maps aircraft sections most vulnerable to lightning, including on planes with experimental designs.
New updates include a small but noteworthy batch of fixes and features.
New updates include a small but noteworthy batch of fixes and features.
New updates include a small but noteworthy batch of fixes and features.
