Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Akhundov, Adnan"'
We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state
Externí odkaz:
http://arxiv.org/abs/1910.06205
Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the applicability of neural nets to set-valued inputs---Deep Set learning
Externí odkaz:
http://arxiv.org/abs/1903.07348
We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources. To this end, we utilize a universal end-to-end Bi-LSTM-based neural sequence labe
Externí odkaz:
http://arxiv.org/abs/1808.03926