Summary of Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching, by Etrit Haxholli et al.
Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
by Etrit Haxholli, Yeti Z. Gürbüz, Oğul Can, Eli Waxman
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dynamic-optimal-transport-like minimization objective for discrete flows aims to outperform autoregressive models on categorical data distributions by minimizing state transitions during generation. The approach is based on a convex interpolation strategy and uses a minibatch optimization method to optimize the transport cost. This minimization objective is applied to discrete flow matching, which has shown competitive performance with autoregressive models for modeling textual data. Additionally, an upper bound on the perplexity of discrete flow models is proposed as a means of evaluating and comparing performance with other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes new ways to improve continuous diffusion and flow models when working with categorical data, like text. The problem is that these models are good at generating sequences of numbers or characters, but they don’t work well for modeling text. To fix this, the authors suggest a new way to minimize the differences between states during generation, using an optimization method called dynamic-optimal-transport-like minimization objective. This makes it possible to compare the performance of different models and evaluate their ability to generate text. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Optimization » Perplexity
