Probabilistic Deep Learning
29 papers with code • 0 benchmarks • 5 datasets
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DENSE
VQA-HAT
ADVIO
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Latest papers with no code
Probabilistic Deep Learning with Generalised Variational Inference
We study probabilistic Deep Learning methods through the lens of Approximate Bayesian Inference.
Probabilistic Deep Learning for Real-Time Large Deformation Simulations
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions.
Causal Discovery from Conditionally Stationary Time Series
Causal discovery, i. e., inferring underlying causal relationships from observational data, has been shown to be highly challenging for AI systems.
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey
Then, we introduce the structures of BM and RBM.
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers
To support this, we propose hybrid Bayesian neural networks with functional probabilistic layers that encode function (and activation) uncertainty.
Probabilistic partition of unity networks: clustering based deep approximation
We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss.
Bayesian Neural Networks: Essentials
Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural networks to support probabilistic deep learning.
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty.
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms.
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra.
