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Mixture-of-Experts (MoE) large language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challenges. We analyze three MoE compression families: expert pruning, expert merging, and weight reconstruction, and derive structural error bounds showing t...
In quantum machine learning, training a quantum model requires each sample to be prepared as a quantum state by a loading circuit that must be re-executed for every shot at every training step. The total burden therefore scales with both the number of samples and the cost of preparation. Existing approaches reduce the...
This work proposes a novel structure-revealing TN paradigm, termed SVD-inspired TN decomposition (SVDinsTN), which enables the formulation of an energy function that simultaneously considers representation error and TN structure complexity, facilitating the joint optimization of data representation and the identificati...
Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variational distribution and the target. A prior score-VI approach formulates this optimization as an eigenvalue problem, with the variational distribution constructed from l...
A large graph is often available only in part: a crawl stopped by its budget, a panel, a partial dump. When the sampled fraction $s$ is known by design the total edge count follows from $\hat e=e_s/s^2$ and no model is needed. We treat the case where $s$ is unknown and the population size is known. Our main result is a...
Background Opioid-free anesthesia (OFA) has been proposed to reduce opioid-related adverse effects, although its impact on overall postoperative recovery remains uncertain. We evaluated whether OFA improves postoperative quality of recovery compared with opioid-based anesthesia (OA) in elderly patients undergoing lapar...
A robust non-parametric Bayesian method known as the Bayesian adaptive tensor ring decomposition (BATR) method, which accomplishes an adaptive low tensor ring (TR) rank model by incorporating a more advanced generalized hyperbolic (GH) prior into the probabilistic framework, thereby facilitating automatic TR rank deter...
A PAC-Bayesian account in which quantum policy design is reframe around an entanglement--generalization trade-off rather than expressivity alone is given, where entangled circuits consistently generalize worse than non-entangled circuits of equal parameter count.
The contribution is accordingly not a better estimator but a characterization of \emph{when prior-based correction is justified}, plus designs that supply the missing information when it is not, plus designs that supply the missing information when it is not.
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