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Found 19 papers, 9 papers with code
Sample what you cant compress
Our work combines autoencoder representation learning with diffusion and is, to our knowledge, the first to demonstrate the efficacy of jointly learning a continuous encoder and decoder under a diffusion-based loss.
Federated Variational Inference: Towards Improved Personalization and Generalization
We then approximate this process using Variational Inference to train our model efficiently.
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
We then recursively combine the bounds for the elementary functions using an interval arithmetic variant of Taylor-mode automatic differentiation.
Weighted Ensemble Self-Supervised Learning
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning.
PAC$^m$-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
The Bayesian posterior minimizes the "inferential risk" which itself bounds the "predictive risk".
Density of States Estimation for Out-of-Distribution Detection
Drawing on the statistical physics notion of ``density of states,'' the DoSE decision rule avoids direct comparison of model probabilities, and instead utilizes the ``probability of the model probability,'' or indeed the frequency of any reasonable statistic.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Automatic Differentiation Variational Inference with Mixtures
Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning.
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century.
Joint Distributions for TensorFlow Probability
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms.
Hydra: Preserving Ensemble Diversity for Model Distillation
As a result, the diversity of the ensemble predictions, stemming from each member, is lost.
On the Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximating posterior distributions in Bayesian neural networks.
Likelihood Ratios for Out-of-Distribution Detection
We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions.
Uncertainty in the Variational Information Bottleneck
We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data.
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
Fixing a Broken ELBO
Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models.
Deep Variational Information Bottleneck
We present a variational approximation to the information bottleneck of Tishby et al. (1999).
