Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Can, Ogul"'
Outperforming autoregressive models on categorical data distributions, such as textual data, remains challenging for continuous diffusion and flow models. Discrete flow matching, a recent framework for modeling categorical data, has shown competitive
Externí odkaz:
http://arxiv.org/abs/2411.00759
Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding space. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-ba
Externí odkaz:
http://arxiv.org/abs/2209.09060
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning
We propose an end-to-end learning approach to address deinterleaving of patterns in time series, in particular, radar signals. We link signal clustering problem to min-cost flow as an equivalent problem once the proper costs exist. We formulate a bi-
Externí odkaz:
http://arxiv.org/abs/2010.12972
During the training of networks for distance metric learning, minimizers of the typical loss functions can be considered as "feasible points" satisfying a set of constraints imposed by the training data. To this end, we reformulate distance metric le
Externí odkaz:
http://arxiv.org/abs/1907.07585
Publikováno v:
2016 IEEE International Geoscience & Remote Sensing Symposium (IGARSS); 2016, p1603-1606, 4p
