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09/13/2023
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Markov processes are widely used mathematical models for describing dyna...
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08/10/2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in s...
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05/26/2021
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Unstructured neural network pruning algorithms have achieved impressive ...
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04/22/2020
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
Compressed sensing (CS) MRI relies on adequate undersampling of the k-sp...
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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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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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08/15/2019
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Limitations on bandwidth and power consumption impose strict bounds on d...
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05/28/2019
Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning
We propose a novel deep learning method for local self-supervised repres...
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10/12/2018
Predictive Uncertainty through Quantization
High-risk domains require reliable confidence estimates from predictive ...
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07/02/2018
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
We propose a semantic segmentation model that exploits rotation and refl...
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06/08/2018