PGBN infers multilayer representations of count vectors using Gibbs sampling.
problem Inferring multilayer representations of high-dimensional count vectors.
method PGBN factorizes layers into product of weight matrices and hidden units, trained with Gibbs sampler.
result PGBN can add more layers to improve performance over Poisson factor analysis.
GBN infers deep representations from discrete vectors.
problem Inferring multilayer deep representations of high-dimensional data.
method GBN factorizes layers into sparse weight matrices and hidden units, trained with Gibbs sampler.
result GBN can improve performance in feature extraction and prediction.
CPGBN analyzes text sequences, capturing word order for better topic extraction.
problem Lack of word order in traditional text representations.
method CPFA processes words as sequences, CPGBN adds hierarchical topic modeling.
result CPGBN extracts high-quality latent representations capturing word order.
Bayesian attention improves model performance and robustness.
problem Limited exploration of stochastic attention in neural networks.
method Introduces Bayesian attention belief networks using gamma and Weibull distributions.
result Outperforms deterministic and stochastic attention methods in accuracy and robustness.
Bayesian method improves few-shot classification accuracy.
problem Few-shot classification with small labeled datasets.
method Gaussian process classifier with Pólya-Gamma augmentation and one-vs-each softmax.
result Improved accuracy and uncertainty quantification.
Develops a deep generative model for radar target recognition using HRRP data.
problem Automatic target recognition in radar systems using high-resolution range profiles.
method Recurrent gamma belief network (rGBN) with hybrid stochastic-gradient MCMC and variational inference.
result Efficient and accurate classification with interpretable latent structure.
New framework analyzes belief evolution in social networks.
problem Analyzing belief evolution in social networks.
method Proposes a new theoretical framework using Markov chain theory for horizontal and vertical transmission.
result Homophily-based networks do not converge to a single set of beliefs.
Trains neural nets for gamma hedging with model uncertainty.
problem Gamma hedging with model mismatch.
method Trains neural networks using loss functions that reward model uncertainty.
result Networks can learn optimal gamma hedging even with model mismatch.
Beta and gamma rhythms mediate different maturation trajectories of cortical networks.
problem Understanding how distinct cortical rhythms influence network maturation.
method Magnetoencephalography (MEG) to map frequency band-specific maturation from age 7 to 29 in 162 participants.
result Beta band mediated networks follow a linear trajectory, while gamma band networks follow an asymptotic one.
The paper analyzes network models with binary values and sub-Gamma noise, deriving asymptotic properties.
problem Analyzing network models with binary values and sub-Gamma noise.
method Derives asymptotic properties of network models with binary values and sub-Gamma noise.
result Established asymptotic consistency and normality of parameter estimators in network models.
Belief Propagation solves a relaxed network flow problem.
problem Generalized Min-Cost Network Flow with relaxed flow conservation constraints.
method Extends Belief Propagation to solve a new class of network flow problems.
result Belief Propagation converges to the exact solution of the relaxed network flow problem.
Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single…
New method learns belief representations for GAIL in POMDPs.
problem Imitation learning in partially observable Markov decision processes (POMDPs).
method Joint learning of belief module and policy with task-aware imitation loss and belief regularization.
result Our BMIL approach outperforms GAIL and task-agnostic belief learning.
Bayesian Experience Reuse improves learning from multiple experts.
problem Learning from multiple experts with conflicting goals.
method Bayesian neural networks with shared features to model uncertainty and derive a probability distribution over expert models.
result BERS method effectively samples demonstrations from the derived distribution to reuse them in new tasks.
Improved error correction using neural networks and belief propagation.
problem Inference in factor graphs with loops or poor approximations.
method Hybrid model combining FG-GNN and belief propagation.
result Hybrid model outperforms belief propagation in error correction tasks.
SOLBP extends efficient inference to uncertain Bayesian networks.
problem Inference in uncertain Bayesian networks with second-order probabilities.
method Extends Loopy Belief Propagation to second-order Bayesian networks.
result Generates inferences consistent with sum-product networks, more efficient and scalable.
Deep learning improves gamma-ray energy estimation and event selection.
problem Improving gamma-ray event selection and energy estimation.
method Adapted convolutional neural networks (CNN) for gamma-ray astronomy.
result Significant improvement in gamma-ray energy estimation and event selection.
We show, analytically and numerically, that wealth distribution in the Bouchaud-Mézard network model of the economy is described by a three-parameter generalized inverse gamma distribution. In the mean-field limit of a network with any two agents linked, it reduces to the inverse gamma distribution.
Deep belief networks can approximate any multivariate density with binary hidden units.
problem Approximating multivariate probability densities with binary hidden units.
method Sharp quantitative bounds on approximation error in terms of hidden units.
result Deep belief networks can approximate any multivariate density with binary hidden units under mild integrability requirements.
Proposes a method for training Bayesian neural networks using synthetic data from Raman and CARS spectra.
problem Limited real observations in Raman and CARS spectroscopy.
method Log-Gaussian Gamma Processes and Bayesian Neural Networks.
result Trained Bayesian neural networks provide accurate estimates of Raman and CARS spectra with uncertainty quantification.
Recurrent networks learn beliefs from history in partially observable environments.
problem Learning optimal policies in partially observable environments.
method Trained recurrent neural networks to approximate value functions, measuring mutual information between hidden states and beliefs.
result Recurrent networks' hidden states correlate with beliefs of relevant state variables, improving expected return.
This paper provides a tutorial on Boltzmann Machines and Deep Belief Networks.
problem Understanding and applying Boltzmann Machines and Deep Belief Networks.
method Explains the structures, conditional distributions, Gibbs sampling, training methods, and deep belief networks of RBMs.
result Comprehensive overview of RBMs and DBNs, useful in various fields.
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths …
CNNs improve particle identification in ground-based gamma-ray astronomy.
problem Identifying particles in gamma-ray astronomy images.
method Used convolutional neural networks (CNNs) with PyTorch and TensorFlow.
result Improved accuracy in identifying gamma-rays and background particles.
A new method for scalable inference in deep discrete LVMs.
problem Challenges in scalable inference for deep discrete latent variable models.
method Topic-layer-adaptive stochastic gradient Riemannian MCMC (TLASGR) for DLDA.
result State-of-the-art results on big data sets.
Develops scalable autoencoder for document networks.
problem Sparse and skewed latent node representations in document relational networks.
method Combines graph Poisson factor analysis with Weibull-based graph inference networks.
result Extracts high-quality hierarchical latent document representations.
Privacy-preserving Bayesian inference framework for sensitive data.
problem Protecting sensitive information in Bayesian data analysis.
method Differential privacy framework for Variational Bayes, tailored to CE and non-CE models.
result Effective privatization of VB for CE models and improved privacy for non-CE models.
Paper applies variational Bayes to gamma distributions for sparse, non-negative data.
problem Inference in models with gamma-distributed latent variables.
method Uses gradient of log posterior for gamma variational distributions.
result Outperforms Gaussian-based methods on network and factor analysis models.
When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observ…
Deep belief network improves smartphone activity recognition.
problem Activity recognition on mobile devices.
method Categorization through deep belief network.
result 98.25% correct diagnosis in training data, 93.01% in test data.
Adversarial meta-learning computes Gamma-minimax estimators for vague prior knowledge.
problem Estimating parameters with vague prior knowledge.
method Adversarial meta-learning algorithms for Gamma-minimax estimators.
result Convergence guarantees and neural network class for selection.
Improved classification using deep belief networks with modified loss functions.
problem Enhancing classification accuracy through unsupervised and supervised learning phases.
method Developed supervised models based on deep belief networks, modified loss functions, and introduced weight bounds.
result Proposed models outperform the two-phase training approach in computational studies.
Deep Belief Network reduces false positives in risky host detection.
problem Overwhelming alerts from SIEM systems, focusing on reducing false positives.
method Combines alert information, logs, and analyst results; uses text mining and graph-based methods for feature generation; compares against various machine learning algorithms.
result Deep Belief Network outperforms other algorithms, improving detection by six times.
For finite networks, Bouchaud-Mézard model's steady state is lognormal and quasi-stationary.
problem Finite network effects on steady state distribution in Bouchaud-Mézard model.
method Analysis of Bouchaud-Mézard model with finite number of nodes.
result Time-dependent lognormal mean and quasi-stationary inverse gamma distribution.
Proposes ECS-DBN for cost-sensitive deep belief network in imbalanced classification.
problem Imbalanced data classification with unequal misclassification costs.
method ECS-DBN uses adaptive differential evolution to optimize misclassification costs based on training data.
result ECS-DBN consistently outperforms state-of-the-art methods on benchmark and real-world datasets.
Factored TSBN improves sequence learning with side information.
problem Learning temporal dependencies in multiple sequences.
method Introducing a three-way weight tensor and factoring transition matrices.
result Achieves state-of-the-art performance on sequential data.
Agents learn state without recalling private signals in networks.
problem Agents learn unknown state from private signals in networks.
method Memoryless update rules that replicate Bayesian agents' beliefs.
result Exponential learning rate similar to Bayesian agents.
Deep belief networks improve Dyna-style planning in large state spaces.
problem Lack of real data and difficulty in learning a good generative model for large state spaces.
method Used deep belief networks to learn an environment model for Dyna-style planning.
result Deep belief networks significantly outperform linear expectation models in empirical validation.
Paper tackles invariance of demodulation in shallow water acoustic communications.
problem Frequency-selective signal distortion (Doppler effect) in shallow water environments.
method Developed ML-based demodulation methods using DBN-NN and DBN-CNN.
result Demonstrated invariance of the proposed method to Doppler effect with 2dB error margin.
Deep BSDE method for pricing and hedging complex financial portfolios.
problem Simultaneous pricing and delta-gamma hedging of large portfolios of multi-asset Bermudan options.
method Discretely reflected BSDEs, One Step Malliavin scheme, neural network regression Monte Carlo method.
result Efficient and accurate pricing and hedging strategies for high-dimensional portfolios.
Modeling volatility with Chained Gamma Distributions for financial time series.
problem Volatility clustering in financial time series, especially in estimating temporal autocorrelation of logarithmic variance of returns.
method Dynamic Bayesian Network with conjugate prior relation of normal-gamma and gamma-gamma, using variational methods for quick approximate solutions.
result The model can express heavier tails than Gaussians, achieving positive excess kurtosis, and runs faster than Monte Carlo methods.
Introduces epistemic deep learning for better uncertainty estimation in neural networks.
problem Uncertainty quantification in deep neural networks.
method Random-set convolutional neural networks with belief function-based loss functions.
result Epistemic approach produces better performance in uncertainty estimation.
PBN combines generative and discriminative capabilities in a neural network.
problem Combining generative and discriminative capabilities in neural networks.
method Convolutional PBN, sharing FF-NN embodiment, combining generative and discriminative qualities.
result PBN shows excellent qualities from either generative or discriminative viewpoint.
Deep TSBNs learn sequential data with hierarchical SBNs and scalable learning.
problem Learning sequential dependencies in time-series data.
method Multi-layered hierarchical sigmoid belief networks (TSBNs) with scalable learning algorithms.
result Achieves state-of-the-art predictive performance and sequence synthesis.
Proposes a feed-forward method for uncertainty propagation in neural networks.
problem Uncertainty in neural network inputs and activations.
method Feed-forward inference method for belief and neural networks, considering both mean and variance.
result Propagates uncertainty through neural network layers and parameters.
Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underly…
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption t…
Deep learning depends on tuning layers near critical points.
problem Understanding how deep learning architectures depend on tuning parameters.
method Random energy approach to analyze statistical dependence in deep belief networks.
result Statistical dependence can propagate only if layers are tuned near critical points.