Ogul Can
13 papers 23 citations h-index 4 2016–2026
Efficient Perplexity Bound and Ratio Matching in Discrete Diffusion Language Models
While continuous diffusion models excel in modeling continuous distributions, their application to categorical data has been less effective. Recent work has shown that ratio-matching through...
Deep Metric Learning with Chance Constraints
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...
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...
Deep Metric Learning with Alternating Projections onto Feasible Sets
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...
Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unlike continuous flow matching, the rectification...
Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often...