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06/29/2023
Understanding Pathologies of Deep Heteroskedastic Regression
Several recent studies have reported negative results when using heteros...
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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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04/13/2023
Asymmetrically-powered Neural Image Compression with Shallow Decoders
Neural image compression methods have seen increasingly strong performan...
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03/10/2023
Deep Anomaly Detection on Tennessee Eastman Process Data
This paper provides the first comprehensive evaluation and analysis of m...
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03/03/2023
Generative Diffusions in Augmented Spaces: A Complete Recipe
Score-based Generative Models (SGMs) have achieved state-of-the-art synt...
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02/15/2023
Zero-Shot Anomaly Detection without Foundation Models
Anomaly detection (AD) tries to identify data instances that deviate fro...
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02/15/2023
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly ...
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02/09/2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Autoencoders and their variants are among the most widely used models in...
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11/15/2022
Probabilistic Querying of Continuous-Time Event Sequences
Continuous-time event sequences, i.e., sequences consisting of continuou...
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10/12/2022
Predictive Querying for Autoregressive Neural Sequence Models
In reasoning about sequential events it is natural to pose probabilistic...
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09/14/2022
Lossy Image Compression with Conditional Diffusion Models
Denoising diffusion models have recently marked a milestone in high-qual...
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05/27/2022
Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse are...
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03/16/2022
SC2: Supervised Compression for Split Computing
Split computing distributes the execution of a neural network (e.g., for...
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03/16/2022
Diffusion Probabilistic Modeling for Video Generation
Denoising diffusion probabilistic models are a promising new class of ge...
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02/17/2022
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
We present a generic way to hybridize physical and data-driven methods f...
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02/16/2022
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Anomaly detection aims at identifying data points that show systematic d...
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02/14/2022
An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machi...
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02/08/2022
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is...
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12/01/2021
Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs
Understanding the details of small-scale convection and storm formation ...
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11/23/2021
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Rate-distortion (R-D) function, a key quantity in information theory, ch...
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11/23/2021
Lossless Compression with Probabilistic Circuits
Despite extensive progress on image generation, deep generative models a...
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08/21/2021
Supervised Compression for Resource-constrained Edge Computing Systems
There has been much interest in deploying deep learning algorithms on lo...
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07/28/2021
Insights from Generative Modeling for Neural Video Compression
While recent machine learning research has revealed connections between ...
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07/19/2021
Structured Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) is considered the ...
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03/30/2021
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e.g. rotations, reflections, and cropping) play an...
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12/15/2020
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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11/06/2020
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Continuous-time event data are common in applications such as individual...
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10/26/2020
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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10/20/2020
Variational Dynamic Mixtures
Deep probabilistic time series forecasting models have become an integra...
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10/19/2020
Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in seque...
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10/07/2020
Improving Sequential Latent Variable Models with Autoregressive Flows
We propose an approach for improving sequence modeling based on autoregr...
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07/03/2020
Generative Modeling for Atmospheric Convection
To improve climate modeling, we need a better understanding of multi-sca...
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06/07/2020
Improving Inference for Neural Image Compression
We consider the problem of lossy image compression with deep latent vari...
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02/18/2020
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding
Deep Bayesian latent variable models have enabled new approaches to both...
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02/15/2020
Extreme Classification via Adversarial Softmax Approximation
Training a classifier over a large number of classes, known as 'extreme ...
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02/07/2020
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 approximatin...
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02/06/2020
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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01/29/2020
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the non-ideality of liquid...
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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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09/30/2019
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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08/30/2019
Autoregressive Text Generation Beyond Feedback Loops
Autoregressive state transitions, where predictions are conditioned on p...
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07/09/2019
Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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07/04/2019
A Quantum Field Theory of Representation Learning
Continuous symmetries and their breaking play a prominent role in contem...
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07/01/2019
Augmenting and Tuning Knowledge Graph Embeddings
Knowledge graph embeddings rank among the most successful methods for li...
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10/05/2018
Deep Probabilistic Video Compression
We propose a variational inference approach to deep probabilistic video ...
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07/24/2018
Iterative Amortized Inference
Inference models are a key component in scaling variational inference to...
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07/04/2018
Quasi-Monte Carlo Variational Inference
Many machine learning problems involve Monte Carlo gradient estimators. ...
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04/08/2018
Active Mini-Batch Sampling using Repulsive Point Processes
The convergence speed of stochastic gradient descent (SGD) can be improv...
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03/21/2018
Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
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03/08/2018
