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Found 3 papers, 3 papers with code
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.
Generative Modeling with Flow-Guided Density Ratio Learning
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradient flow of entropy-regularized f-divergences introduced in DGflow.
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Learning accurate predictive models of real-world dynamic phenomena (e. g., climate, biological) remains a challenging task.
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