Dynamic negatives rely upon conditioning a DPP on a cho- sen sample A (see Alg. 1: PLk (A [{j}) can be efficiently computed for all j by a preprocessing ...

Determinantal point processes (DPPs) offer a promising and com- plementary ... Blei and J. D. Lafferty, “Dynamic topic models,” in Proceedings of the.

In comparison, the dynamic DPPNET models DPPs with kernels that depend arbitrarly on Φ, including kernels with kernel functions too costly to be computed on- ...

Oct 26, 2023 · PDF | The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions on random sets defined on a finite ...

The most relevant works here appear in the domain of submodular optimization and composable coresets, where algorithms have been developed for both dynamic.

Dynamic Determinantal Point Processes. In AAAI, 2018. [29] Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. Training region-based object detec- tors ...

but the eigendecompositions and dynamic programming used in practice typically take cubic time. 1332. Page 6. Efficient Sampling for k-Determinantal Point ...

Duration: 32:44
Posted: Jun 10, 2018

Through an evaluation on several real-world datasets, it is shown experimentally that the deep DPP can provide a considerable improvement in the predictive ...

Determinantal point processes (DPPs) are random point processes well-suited for ... dimensional spaces with complex non-linear dynamics. A typical approximation ...