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1 code implementation • ICML 2020 • Yibo Yang, Robert Bamler, Stephan Mandt

Deep Bayesian latent variable models have enabled new approaches to both model and data compression.

1 code implementation • 16 Mar 2022 • Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

Split computing distributes the execution of a neural network (e. g., for a classification task) between a mobile device and a more powerful edge server.

no code implementations • 16 Mar 2022 • Ruihan Yang, Prakhar Srivastava, Stephan Mandt

Denoising diffusion probabilistic models are a promising new class of generative models that are competitive with GANs on perceptual metrics.

no code implementations • 17 Feb 2022 • Fabian Jirasek, Robert Bamler, Stephan Mandt

We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the data-driven and physical baselines and established ensemble methods from the machine learning literature.

no code implementations • 16 Feb 2022 • Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.

1 code implementation • 14 Feb 2022 • Yibo Yang, Stephan Mandt, Lucas Theis

Neural compression is the application of neural networks and other machine learning methods to data compression.

1 code implementation • 8 Feb 2022 • Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.

no code implementations • 1 Dec 2021 • Harshini Mangipudi, Griffin Mooers, Mike Pritchard, Tom Beucler, Stephan Mandt

Understanding the details of small-scale convection and storm formation is crucial to accurately represent the larger-scale planetary dynamics.

1 code implementation • ICLR 2022 • Anji Liu, Stephan Mandt, Guy Van Den Broeck

To overcome such problems, we establish a new class of tractable lossless compression models that permit efficient encoding and decoding: Probabilistic Circuits (PCs).

no code implementations • ICLR 2022 • Yibo Yang, Stephan Mandt

By contrast, this paper makes the first attempt at an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source requiring only i. i. d.

no code implementations • pproximateinference AABI Symposium 2022 • Antonios Alexos, Alex James Boyd, Stephan Mandt

Unfortunately, VI makes strong assumptions on both the factorization and functional form of the posterior.

1 code implementation • 21 Aug 2021 • Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.

no code implementations • 28 Jul 2021 • Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images.

no code implementations • 19 Jul 2021 • Antonios Alexos, Alex Boyd, Stephan Mandt

Since practitioners face speed versus accuracy tradeoffs in these models, variational inference (VI) is often the preferable option.

1 code implementation • 30 Mar 2021 • Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

Data transformations (e. g. rotations, reflections, and cropping) play an important role in self-supervised learning.

no code implementations • ICLR Workshop Neural_Compression 2021 • Yibo Yang, Stephan Mandt

The rate-distortion function tells us the minimal number of bits on average to compress a random object within a given distortion tolerance.

no code implementations • ICLR Workshop Neural_Compression 2021 • Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

There has been a recent surge of interest in neural video compression models that combines data-driven dimensionality reduction with learned entropy coding.

no code implementations • NeurIPS 2021 • Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity.

no code implementations • pproximateinference AABI Symposium 2021 • Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.

no code implementations • pproximateinference AABI Symposium 2021 • Aodong Li, Alex James Boyd, Padhraic Smyth, Stephan Mandt

We consider the problem of online learning in the presence of sudden distribution shifts, which may be hard to detect and can lead to a slow but steady degradation in model performance.

1 code implementation • NeurIPS 2020 • Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence.

1 code implementation • 26 Oct 2020 • Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors.

no code implementations • 20 Oct 2020 • Chen Qiu, Stephan Mandt, Maja Rudolph

Deep probabilistic time series forecasting models have become an integral part of machine learning.

1 code implementation • ICLR 2021 • Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.

no code implementations • 7 Oct 2020 • Joseph Marino, Lei Chen, JiaWei He, Stephan Mandt

We propose an approach for improving sequence modeling based on autoregressive normalizing flows.

no code implementations • 3 Jul 2020 • Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael Pritchard, Tom Beucler

While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources.

1 code implementation • NeurIPS 2020 • Yibo Yang, Robert Bamler, Stephan Mandt

We consider the problem of lossy image compression with deep latent variable models.

2 code implementations • ICML 2020 • Yibo Yang, Robert Bamler, Stephan Mandt

Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single standard VAE.

1 code implementation • ICLR 2020 • Robert Bamler, Stephan Mandt

Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce.

no code implementations • ICML 2020 • Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights.

1 code implementation • ICML 2020 • Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.

no code implementations • 29 Jan 2020 • Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay, Robert A. Vandermeulen, Robert Bamler, Michael Bortz, Stephan Mandt, Marius Kloft, Hans Hasse

Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes.

no code implementations • 14 Jan 2020 • Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton

As a result, the diversity of the ensemble predictions, stemming from each member, is lost.

no code implementations • pproximateinference AABI Symposium 2019 • Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt

Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.

Dimensionality Reduction
Multivariate Time Series Imputation
**+2**

no code implementations • 30 Sep 2019 • Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

In this paper, we revisit perturbation theory as a powerful way of improving the variational approximation.

no code implementations • 25 Sep 2019 • Jakub Świątkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin

Variational Bayesian Inference is a popular methodology for approximating posterior distributions in Bayesian neural networks.

1 code implementation • IJCNLP 2019 • Florian Schmidt, Stephan Mandt, Thomas Hofmann

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models.

1 code implementation • 9 Jul 2019 • Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt

Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.

Dimensionality Reduction
Multivariate Time Series Imputation
**+1**

no code implementations • 4 Jul 2019 • Robert Bamler, Stephan Mandt

Continuous symmetries and their breaking play a prominent role in contemporary physics.

1 code implementation • 1 Jul 2019 • Robert Bamler, Farnood Salehi, Stephan Mandt

Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i. e., the task of completing an incomplete collection of relational facts.

Ranked #4 on Link Prediction on FB15k

no code implementations • NeurIPS 2019 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy.

no code implementations • 27 Sep 2018 • Farnood Salehi, Robert Bamler, Stephan Mandt

We develop a probabilistic extension of state-of-the-art embedding models for link prediction in relational knowledge graphs.

no code implementations • 27 Sep 2018 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt

We propose a variational inference approach to deep probabilistic video compression.

1 code implementation • ICML 2018 • Joseph Marino, Yisong Yue, Stephan Mandt

The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.

no code implementations • ICML 2018 • Alexander Buchholz, Florian Wenzel, Stephan Mandt

We also propose a new algorithm for Monte Carlo objectives, where we operate with a constant learning rate and increase the number of QMC samples per iteration.

no code implementations • ICML 2018 • Robert Bamler, Stephan Mandt

We show that representation learning models for time series possess an approximate continuous symmetry that leads to slow convergence of gradient descent.

1 code implementation • 8 Apr 2018 • Cheng Zhang, Cengiz Öztireli, Stephan Mandt, Giampiero Salvi

We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes.

1 code implementation • 21 Mar 2018 • Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt

First, we extend the class of tractable priors from Wiener processes to the generic class of Gaussian processes (GPs).

3 code implementations • ICML 2018 • Yingzhen Li, Stephan Mandt

This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features.

no code implementations • 8 Mar 2018 • Robert Bamler, Stephan Mandt

We show that representation learning models for time series possess an approximate continuous symmetry that leads to slow convergence of gradient descent.

no code implementations • ICLR 2018 • Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.

no code implementations • ICLR 2018 • Joseph Marino, Yisong Yue, Stephan Mandt

Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs).

no code implementations • 15 Nov 2017 • Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models.

no code implementations • 10 Nov 2017 • Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt

Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words.

no code implementations • NeurIPS 2017 • Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt

Black box variational inference (BBVI) with reparameterization gradients triggered the exploration of divergence measures other than the Kullback-Leibler (KL) divergence, such as alpha divergences.

no code implementations • 4 Jul 2017 • Robert Bamler, Stephan Mandt

Black box variational inference with reparameterization gradients (BBVI) allows us to explore a rich new class of Bayesian non-conjugate latent time series models; however, a naive application of BBVI to such structured variational models would scale quadratically in the number of time steps.

no code implementations • CVPR 2017 • Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori

Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data.

no code implementations • 1 May 2017 • Cheng Zhang, Hedvig Kjellstrom, Stephan Mandt

The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data.

1 code implementation • 13 Apr 2017 • Stephan Mandt, Matthew D. Hoffman, David M. Blei

Specifically, we show how to adjust the tuning parameters of constant SGD to best match the stationary distribution to a posterior, minimizing the Kullback-Leibler divergence between these two distributions.

1 code implementation • ICML 2017 • Robert Bamler, Stephan Mandt

We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time.

no code implementations • NeurIPS 2016 • Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data.

no code implementations • 8 Feb 2016 • Stephan Mandt, Matthew D. Hoffman, David M. Blei

With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution.

no code implementations • 16 Jul 2015 • Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes.

no code implementations • 7 Nov 2014 • Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Blei

Lastly, we develop local variational tempering, which assigns a latent temperature to each data point; this allows for dynamic annealing that varies across data.

no code implementations • NeurIPS 2014 • Stephan Mandt, David Blei

It uses stochastic optimization to fit a variational distribution, following easy-to-compute noisy natural gradients.

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