UK newspapers responded to a major climate ruling by the world's top court by claiming "lawfare" and linking the decision to ...
Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...
The behavior of language models is influenced by the prior context provided in prompts. Depending on whether you pick synthesis or shake, the next row looks very different — Vishal Misra Contextual ...
Understanding the differences between sparse data and dense data is crucial in various fields, including natural language processing, healthcare, and sensor networks. These two types of data are ...
KokkosKernels implements local computational kernels for linear algebra and graph operations, using the Kokkos shared-memory parallel programming model. "Local" means not using MPI, or running within ...
Abstract: We present a new Gaussian process (GP) inference algorithm, called online sparse matrix Gaussian processes (OSMGP), and demonstrate its merits by applying it to the problems of head pose ...
Quantum computing has the potential to revolutionize a wide range of scientific fields, including cryptography, drug discovery, climate modeling, finance, and artificial intelligence. Unlike classical ...
Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse ...
Anomaly detection (AD) has emerged as a prominent area of research in hyperspectral imagery (HSI) processing. Traditional algorithms, such as low-rank and sparse matrix decomposition (LRaSMD), often ...
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