5 citations · 8 across the 4 of their papers we have counts for
4 papers
Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Etrit Haxholli, Yeti Z. Gurbuz, Ogul Can +1
Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unlike continuous flow matching, th…
Generalized Sum Pooling for Metric Learning
Yeti Z. Gurbuz, Ozan Sener, A. Aydın Alatan
A common architectural choice for deep metric learning is a convolutional neural network followed by global average pooling (GAP). Albeit simple, GAP is a highly effective way to a…
Deep Metric Learning with Chance Constraints
Yeti Z. Gurbuz, Ogul Can, A. Aydin Alatan
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 pr…
Deep Metric Learning with Alternating Projections onto Feasible Sets
Oğul Can, Yeti Ziya Gürbüz, A. Aydın Alatan
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 impose…