Determinantal Point Processes
PulseAugur coverage of Determinantal Point Processes — every cluster mentioning Determinantal Point Processes across labs, papers, and developer communities, ranked by signal.
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Privacy-Preserving CT Slice Generation Framework Unveiled
Researchers from DS@GT ARC have developed a novel framework for generating synthetic lung CT slices that prioritizes privacy. Their approach integrates Optimal Transport Conditional Flow Matching with a post-generation …
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New sampling methods improve efficiency for complex distributions · 2 sources tracked
Researchers have developed a new method called Gradient-free Riemannian Langevin Sampler (GRiLS) to improve the efficiency of sampling multimodal probability distributions. This approach aims to overcome limitations in …
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New framework for dataset appraisal offers 35,000x speedup
Researchers have introduced matrix spectral functions as a broader class of objectives for dataset appraisal, encompassing the Vendi Score and determinantal point processes (DPPs). They demonstrated that these functions…
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New DPP kernels improve ML minibatches with wavelets
Researchers have developed new Determinantal Point Processes (DPPs) using wavelets to improve minibatch generation for machine learning tasks. These novel DPPs offer provably better accuracy guarantees and a general met…