
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e.g. rotations, reflections, and cropping) play an...
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Variational Beam Search for Online Learning with Distribution Shifts
We consider the problem of online learning in the presence of sudden dis...
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UserDependent Neural Sequence Models for ContinuousTime Event Data
Continuoustime event data are common in applications such as individual...
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Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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Variational Dynamic Mixtures
Deep probabilistic time series forecasting models have become an integra...
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Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in seque...
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Improving Sequential Latent Variable Models with Autoregressive Flows
We propose an approach for improving sequence modeling based on autoregr...
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Generative Modeling for Atmospheric Convection
To improve climate modeling, we need a better understanding of multisca...
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Improving Inference for Neural Image Compression
We consider the problem of lossy image compression with deep latent vari...
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VariableBitrate Neural Compression via Bayesian Arithmetic Coding
Deep Bayesian latent variable models have enabled new approaches to both...
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Extreme Classification via Adversarial Softmax Approximation
Training a classifier over a large number of classes, known as 'extreme ...
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The ktied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the nonideality of liquid...
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Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Variational inference has become one of the most widely used methods in ...
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Autoregressive Text Generation Beyond Feedback Loops
Autoregressive state transitions, where predictions are conditioned on p...
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Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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A Quantum Field Theory of Representation Learning
Continuous symmetries and their breaking play a prominent role in contem...
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Augmenting and Tuning Knowledge Graph Embeddings
Knowledge graph embeddings rank among the most successful methods for li...
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Deep Probabilistic Video Compression
We propose a variational inference approach to deep probabilistic video ...
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Iterative Amortized Inference
Inference models are a key component in scaling variational inference to...
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QuasiMonte Carlo Variational Inference
Many machine learning problems involve Monte Carlo gradient estimators. ...
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Active MiniBatch Sampling using Repulsive Point Processes
The convergence speed of stochastic gradient descent (SGD) can be improv...
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Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
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Improving Optimization in Models With Continuous Symmetry Breaking
Many loss functions in representation learning are invariant under a con...
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A Deep Generative Model for Disentangled Representations of Sequential Data
We present a VAE architecture for encoding and generating high dimension...
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Advances in Variational Inference
Many modern unsupervised or semisupervised machine learning algorithms ...
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Bayesian Paragraph Vectors
Word2vec (Mikolov et al., 2013) has proven to be successful in natural l...
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Perturbative Black Box Variational Inference
Black box variational inference (BBVI) with reparameterization gradients...
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Structured Black Box Variational Inference for Latent Time Series Models
Continuous latent time series models are prevalent in Bayesian modeling;...
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Determinantal Point Processes for MiniBatch Diversification
We study a minibatch diversification scheme for stochastic gradient des...
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Stochastic Gradient Descent as Approximate Bayesian Inference
Stochastic Gradient Descent with a constant learning rate (constant SGD)...
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Dynamic Word Embeddings
We present a probabilistic language model for timestamped text data whi...
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Exponential Family Embeddings
Word embeddings are a powerful approach for capturing semantic similarit...
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A Variational Analysis of Stochastic Gradient Algorithms
Stochastic Gradient Descent (SGD) is an important algorithm in machine l...
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Variational Tempering
Variational inference (VI) combined with data subsampling enables approx...
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Smoothed Gradients for Stochastic Variational Inference
Stochastic variational inference (SVI) lets us scale up Bayesian computa...
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