Submodular maximization is a significant area of interest in combinatorial optimization, with numerous real-world applications. A research team led by Xiaoming SUN from the State Key Lab of Processors ...

Notre Dame and Texas A&M are among the postseason hopefuls awaiting a committee call. Michael Reaves / Getty Images A season marked by upheaval, uncertainty and maximum-capacity theater is nearing its ...

Understanding the differences between probabilistic and deterministic AI will help manufacturers make more informed choices and achieve measurable results. As professionals become interested in using ...

For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and ...

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

import torch @torch.compile(backend="inductor") def fn(src, index, base_tensor): src = src + 10 torch.use_deterministic_algorithms(True) base_tensor.scatter_(0, index ...

Differential privacy (DP) stands as the gold standard for protecting user information in large-scale machine learning and data analytics. A critical task within DP is partition selection—the process ...

After rating comfortably behind My Boy Prince early, Deterministic quickly overwhelmed that front-runner and drew off in the stretch to post his most impressive win to date Aug. 2 in the $750,000 ...