arXiv:2509.01370v2 Announce Type: replace Abstract: Nowadays, the nanostructure inverse problem is an attractive problem that helps researchers to understand the relationship between the properties and the structure of nanomaterials. This article focuses on the problem of using PDF to recover…
arXiv:2509.14723v1 Announce Type: new Abstract: Single-cell foundation models (scFMs) have demonstrated state-of-the-art performance on various tasks, such as cell-type annotation and perturbation response prediction, by learning gene regulatory networks from large-scale transcriptome data. However, a significant challenge remains: the decision-making…
arXiv:2210.06459v3 Announce Type: replace-cross Abstract: Statistical tools which satisfy rigorous privacy guarantees are necessary for modern data analysis. It is well-known that robustness against contamination is linked to differential privacy. Despite this fact, using multivariate medians for differentially private and…
arXiv:2509.14724v1 Announce Type: new Abstract: In light of their capability to capture structural information while reducing computing complexity, anchor graph-based multi-view clustering (AGMC) methods have attracted considerable attention in large-scale clustering problems. Nevertheless, existing AGMC methods still face the following…
arXiv:2411.05869v3 Announce Type: replace-cross Abstract: The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeling, and analyzing real-world measurements of nonlinear processes. Traditional implementations of GPs involve stationary…
arXiv:2509.14775v1 Announce Type: new Abstract: Accurate hourly weather forecasting is critical for numerous applications. Recent deep learning models have demonstrated strong capability on 6-hour intervals, yet achieving accurate and stable hourly predictions remains a critical challenge. This is primarily due…
arXiv:2509.15040v1 Announce Type: cross Abstract: Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast,…
At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.
arXiv:2509.15116v1 Announce Type: cross Abstract: We formalize the multi-graded Proj construction in Lean4, illustrating mechanized mathematics and formalization.
arXiv:2508.21589v2 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset…
