Probabilistic deep learning uses neural networks and models to handle uncertainty.
problem Handling uncertainty in deep learning models.
method Two approaches: probabilistic neural networks and deep probabilistic models.
result TensorFlow Probability library supports both approaches.
PNCs balance tractability and expressiveness in probabilistic modeling.
problem Balancing tractability and expressiveness in probabilistic models.
method Introduce probabilistic neural circuits (PNCs) as a mix of Bayesian networks and neural networks.
result PNCs are powerful function approximators.
ProFnet models HDFTS with neural networks, offering scalable probabilistic forecasts.
problem Modeling high-dimensional functional time series with nonlinear trends and high spatial dimensions.
method Integrates feedforward and deep neural networks with probabilistic modeling.
result Superior performance in forecasting Japan's mortality rates.
Hybrid Bayesian neural networks use function uncertainty for probabilistic inference.
problem Uncertainty in neural network weights is hard to specify and interpret.
method Integrates probabilistic layers with standard deterministic layers for function uncertainty.
result Improves probabilistic inference by encoding function uncertainty.
Innovative PGMs match neural networks, revealing precise approximations during forward propagation.
problem Lack of precise semantics and probabilistic interpretation in neural networks.
method Constructing infinite tree-structured PGMs that correspond to neural networks.
result DNNs perform precise approximations of PGM inference during forward propagation.
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this packag…
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do "compilation of infer…
uGMM-NN integrates probabilistic reasoning into neural networks.
problem Capturing multimodality and uncertainty in neural network activations.
method Parameterizes activations as univariate Gaussian mixtures with learnable parameters.
result Competitive discriminative performance with probabilistic activations.
This work combines recurrent models with diffusion for probabilistic time series forecasting.
problem Scalability and capturing high-dimensional distributions and cross-feature dependencies in time series forecasting.
method Combines recurrent neural networks' efficiency with diffusion models' probabilistic modeling, using stochastic interpolants and conditional generation.
result Offers scalable probabilistic time series forecasting methods.
Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in pa…
Extends branch and bound for probabilistic neural network verification.
problem Probabilistic verification of neural networks.
method Split preactivations, use linear bounds, divide problems iteratively.
result Sound and complete for feedforward-ReLU networks.
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research. However, most of the research literature merely focuses on the \textit{worst-case} setting where the input of the neural …
New method encodes function preferences into neural nets for better generalization.
problem Challenges in encoding explicit function preferences in neural network training.
method Function-space empirical Bayes (FSEB) regularization.
result FSEB leads to near-perfect semantic shift detection and improved generalization.
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very restricted model classes where exact or approximate probabilistic inference were feasible, and (ii) small or medium-sized data sets which fit …
CNNs improve wind speed forecasts in the Netherlands.
problem Limited spatial patterns in current post-processing methods.
method Convolutional Neural Networks (CNNs) for spatial wind speed information.
result CNNs produce better probabilistic forecasts with higher Brier skill scores.
Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is difficult and has not achieved the level of performance of methods such as deep neural networks on many …
A new neural network model MDRBM improves noise-robustness in classification.
problem Improving noise-robustness in classification problems.
method Stacking a probabilistic-ELM layer on a discriminative restricted Boltzmann machine (DRBM).
result MDRBM outperforms other models, especially in noise-robustness.
BBNN improves neural network accuracy and uncertainty quantification.
problem Overfitting and lack of interpretability in probabilistic neural networks.
method Boosted Bayesian Neural Networks (BBNN) using Boosting Variational Inference (BVI).
result BBNN achieves ~5% higher accuracy and superior uncertainty quantification.
Study probabilistic safety of BNNs under adversarial attacks.
problem Evaluate vulnerability of BNNs to adversarial attacks.
method Relaxation techniques from non-convex optimization to compute probabilistic safety bounds.
result Certify probabilistic safety of BNNs with millions of parameters.
Develops a neural framework for probabilistic forecasting of dynamical systems.
problem Uncertainty quantification in dynamical systems using trajectory-oriented approaches.
method D2D neural probabilistic forecasting framework using kernel mean embeddings and mixture density networks.
result The D2D model captures distributional evolution in chaotic systems and produces skillful probabilistic forecasts.
Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group. Much recent research has been devoted to encoding invariance under symmetry transformations into neural n…
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilistic inference can be implemented by a network of spiking neurons in th…
Neural network predicts daily power consumption with high accuracy.
problem Middle-term power consumption prediction in the energy sector.
method Incorporates trend, seasonality, and weather conditions in a shallow Neural Network.
result Excellent density forecast results on one-year test set.
This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochast…
Probabilistic graphical models are a central tool in AI; however, they are generally not as expressive as deep neural models, and inference is notoriously hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expr…
Develops inference combinators for probabilistic programs using neural networks.
problem Creating efficient proposals for probabilistic program inference.
method Inference combinators using neural network parameterization of proposals.
result Correct by construction variational methods tailored to specific models.
Paper analyzes GCNN sensitivity to probabilistic graph perturbations.
problem Investigating how GCNNs handle probabilistic graph errors.
method Establishes error bounds and linear relationships between GSO perturbations and GCNN outputs.
result GCNNs maintain stability under graph edge perturbations if GSO errors are bounded.
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…
Even though probabilistic treatments of neural networks have a long history, they have not found widespread use in practice. Sampling approaches are often too slow already for simple networks. The size of the inputs and the depth of typical CNN architectures in computer vision only compound this problem. Uncertainty in…
Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic pr…
Study introduces a probabilistic framework for air-sea fluxes using neural networks.
problem Accurately quantifying air-sea fluxes for understanding interactions and improving weather/climate models.
method Gaussian distributions conditioned on input variables, artificial neural networks, eddy-covariance data, minimizing negative log-likelihood loss.
result Trained neural networks provide alternative mean flux estimates and quantify uncertainty.
New method uses backward SDEs for deep learning uncertainty.
problem Uncertainty quantification in deep learning models.
method Probabilistic machine learning with stochastic neural networks and stochastic optimal control.
result Effectiveness validated through numerical experiments.
A new method for efficient probabilistic meta-learning.
problem High-quality predictions with well-calibrated uncertainty estimates require large amounts of data.
method Amortised Inference in Bayesian Neural Networks (APOVI-BNN)
result The APOVI-BNN produces high-quality predictions with well-calibrated uncertainty estimates using significantly less data.
A-NeSI scales approximate inference for probabilistic neurosymbolic learning.
problem Combining neural networks with symbolic reasoning for scalable inference.
method A-NeSI: a new framework for PNL using neural networks for approximate inference.
result A-NeSI achieves scalable approximate inference without semantic changes.
nnLDA combines neural and probabilistic methods for better topic modeling with side information.
problem Lack of integration of auxiliary information in traditional topic models.
method nnLDA integrates side information through a neural prior mechanism, optimizing both neural and probabilistic components.
result nnLDA outperforms traditional models in topic coherence, perplexity, and classification.
We consider the probabilistic analogue to neural network matrix factorization (Dziugaite & Roy, 2015), which we construct with Bayesian neural networks and fit with variational inference. We find that a linear model fit with variational inference can attain equivalent predictive performance to the regular neural networ…
Proposes VSGD optimizer combining probabilistic and gradient-based methods.
problem Uncertainty modeling in deep neural networks.
method Combines probabilistic and gradient-based approaches using SVI.
result VSGD outperforms Adam and SGD on image classification tasks.
SkewPNN uses probabilistic neural networks with skew-normal kernels to improve classification of imbalanced data.
problem Imbalanced data distribution leading to biased predictions for minority classes.
method Probabilistic neural networks with skew-normal kernel function and Bat optimization algorithm for hyperparameter tuning.
result SkewPNN and BA-SkewPNN outperform other methods in both balanced and imbalanced datasets.
Bayesian neural networks outperform calibrated neural networks for tabular data.
problem Uncertainty in neural network predictions for tabular data.
method Bayesian neural networks vs. post-hoc calibration methods.
result Bayesian neural networks yield competitive performance compared to calibrated neural networks.
This work proposes an unsupervised neural network framework for solving combinatorial optimization problems on graphs.
problem Challenges in neural networks solving combinatorial optimization problems without labeled instances.
method Inspired by Erdos' probabilistic method, a neural network parametrizes a probability distribution over sets, optimizing it to find low-cost integral solutions.
result The method provides valid solutions to the maximum clique problem and local graph clustering, achieving competitive results.
New method for efficient probabilistic inference using masked language modeling.
problem Efficient posterior inference in probabilistic programs with many hyper-parameters.
method Formulate inference as masked language modeling, train a neural network to unmask random values.
result Foundation posterior for zero-shot inference and fine-tuning across a range of programs.
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural netw…
TDistNNs improve prediction intervals for neural networks by using t-distributions.
problem Traditional neural networks provide only point estimates, lacking predictive uncertainty.
method TDistNNs generate t-distributed outputs with adjustable degrees of freedom, enhancing robustness to non-Gaussian data.
result TDistNNs produce narrower prediction intervals with proper coverage compared to Gaussian-based PNNs.
Small neural networks embed arbitrary metric spaces into Gaussian mixtures.
problem Embedding arbitrary metric spaces into a fixed space with low distortion.
method Probabilistic transformers of small depth and width.
result Embeddings with low metric distortion for various metric spaces.
Proposes CCE to assess point-wise reliability of neural network predictions.
problem Overconfidence and misaligned predictive distributions in neural networks.
method Introduces Conditional Congruence (CCE) metric using conditional kernel mean embeddings.
result CCE exhibits correctness, monotonicity, reliability, and robustness in high-dimensional regression tasks.
LIC compiles probabilistic models to generate efficient MCMC proposals.
problem Creating accurate Metropolis-Hastings proposals for Bayesian inference.
method Integrates probabilistic graphical models and neural networks in an open-source framework to optimize proposal distributions.
result LIC produces more efficient and robust MCMC proposals compared to existing methods.
Bayesian approach models neurodegenerative diseases without clinical labels.
problem Personalized, predictive modeling of neurodegenerative diseases.
method Probabilistic programmed deep kernel learning combining Gaussian processes and neural networks.
result Surpasses deep learning in accuracy and timeliness of predicting neurodegeneration.
PNNs model aleatoric uncertainty in scientific machine learning with high accuracy.
problem Aleatoric uncertainty in scientific systems with unequal variance.
method Developed a probabilistic distance metric to optimize PNN architecture and used it in material science applications.
result PNNs yield remarkably accurate output mean estimates and high correlation in predicted intervals.