
UserDependent Neural Sequence Models for ContinuousTime Event Data
Continuoustime event data are common in applications such as individual...
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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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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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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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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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Improving Optimization in Models With Continuous Symmetry Breaking
Many loss functions in representation learning are invariant under a con...
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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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Dynamic Word Embeddings
We present a probabilistic language model for timestamped text data whi...
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Robert Bamler
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