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Found 6 papers, 5 papers with code
DiffEnc: Variational Diffusion with a Learned Encoder
Diffusion models may be viewed as hierarchical variational autoencoders (VAEs) with two improvements: parameter sharing for the conditional distributions in the generative process and efficient computation of the loss as independent terms over the hierarchy.
Image-free Classifier Injection for Zero-Shot Classification
We achieve this with our proposed Image-free Classifier Injection with Semantics (ICIS) that injects classifiers for new, unseen classes into pre-trained classification models in a post-hoc fashion without relying on image data.
Addressing caveats of neural persistence with deep graph persistence
Whilst this captures useful information for linear classifiers, we find that no relevant spatial structure is present in later layers of deep neural networks, making neural persistence roughly equivalent to the variance of weights.
Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions
Recent efforts have been very successful in accurately mapping welfare in datasparse regions of the world using satellite imagery and other non-traditional data sources.
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds
Empirically, for the training of both continuous and discrete generative models, the proposed method yields superior variance reduction, resulting in an SNR for IWAE that increases with $K$ without relying on the reparameterization trick.
Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds
This paper introduces novel results for the score function gradient estimator of the importance weighted variational bound (IWAE).
