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Found 12 papers, 8 papers with code
TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings
Drawing inspiration from the success of diffusion models in various domains, numerous research papers proposed methods for adapting them to text data.
Star-Shaped Denoising Diffusion Probabilistic Models
In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM.
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation$-$averaging the predictions of a machine learning model across multiple augmented samples of data$-$is a widely used technique that improves the predictive performance.
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand.
Structured Semi-Implicit Variational Inference
In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions.
Variational Dropout via Empirical Bayes
We study the Automatic Relevance Determination procedure applied to deep neural networks.
Doubly Semi-Implicit Variational Inference
Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact.
Variance Networks: When Expectation Does Not Meet Your Expectations
Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging.
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts.
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e. g. removes neurons and/or convolutional channels in CNNs.
Variational Dropout Sparsifies Deep Neural Networks
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout.
