Adding metadata abruptly changes network inference outcomes.
problem Understanding the impact of metadata on network inference.
method Investigated the effect of metadata on network inference problems.
result Metadata causes abrupt transitions in inference outcomes.
Spectral Inference Networks learn eigenfunctions from data using optimization.
problem Learning eigenfunctions of linear operators from data.
method Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators and use stochastic optimization.
result Spectral Inference Networks accurately recover eigenfunctions and discover interpretable representations from video data.
Inference network approximates structured argmax inference for structured prediction.
problem Efficient inference for complex structured prediction problems.
method Trains a neural network to approximate structured argmax inference, using large-margin training criteria.
result Significant speed-ups and improved accuracy compared to gradient descent methods.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
Proposes a new algorithm for efficient probabilistic inference.
problem Efficient probabilistic inference in deep models with graphical structures.
method Structured inference networks and variational message-passing algorithm.
result Enables fast and efficient natural-gradient inference for deep structured models.
Paper shows how to infer hidden states in neural networks analytically.
problem Intractability of Bayesian inference for neural networks.
method Leverage tractable approximate Gaussian inference (TAGI) for hidden states inference.
result Demonstrates inference of hidden states through constraints for various applications.
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the sp…
Bayesian interpolants explain neural network inferences concisely.
problem Understanding neural network inferences.
method Adapting Craig interpolants for neural networks.
result Produces precise, understandable explanations.
Study compares uncertainty estimation methods for Bayesian Neural Networks.
problem Quality of uncertainty quantification in Bayesian Neural Networks.
method Empirical comparison of 10 inference methods on regression and classification tasks.
result Common inference metrics can be misleading, and methods designed to capture posterior structure do not always produce high-quality approximations.
Active learning reduces spin network inference complexity by 10^6-fold.
problem Difficulty in inferring direct interactions in complex networks.
method Information geometry framework to quantify inference difficulty and information gain from perturbations.
result Designed perturbations reduce sampling complexity by 10^6-fold across various network architectures.
Variational inference improves neural network matrix factorization for stochastic blockmodels.
problem Improving predictive performance of neural network matrix factorization for stochastic blockmodels.
method Construct Bayesian neural networks and fit with variational inference.
result Variational inference can achieve equivalent performance to neural networks on Movielens data.
Tackles network structure inference from time series data using GNN.
problem Inferring network structure from incomplete or no information.
method Gumbel Graph Network (GGN) model for network reconstruction and completion.
result GGN can reconstruct up to 100% network structure and infer missing parts with up to 90% accuracy.
Method uses neural networks to speed up probabilistic model inference.
problem Efficient inference in probabilistic models.
method Compiles probabilistic programs into neural networks for approximate inference.
result Significant speedups in inference efficiency demonstrated on various models.
New method for inferring network topology from partial data.
problem Inferring network topology from limited node data.
method Vector autoregressive model and Gaussian mixture algorithm.
result The proposed method converges to the network combination matrix in probability.
Proposes TAGI for efficient Gaussian inference in Bayesian neural networks.
problem Efficient inference in Bayesian neural networks with complex architectures.
method Analytical method for tractable approximate Gaussian inference (TAGI).
result Matches performance of gradient-based methods with O(n) computational complexity. CheckNet verifies neural network inference on untrusted devices.
problem Ensuring secure and tamper-proof inference on untrusted devices.
method A checksum-based approach for neural network inference verification.
result Excellent attack detection and success bounds on various models.
The paper introduces an inference algorithm for graded Bayesian networks.
problem Inference in graded Bayesian networks.
method Tropicalization of the marginal distribution of observed variables, rank-by-rank evaluation of hidden variables.
result Established an inference algorithm for graded Bayesian networks.
A spiking neural network model for probabilistic inference of binary Markov random fields.
problem Implementing probabilistic inference in spiking neural networks.
method Designing a spiking recurrent neural network and proving its equivalence to mean-field inference of binary Markov random fields.
result The spiking neural network model can implement inference of arbitrary binary Markov random fields.
BaMANI uses ensemble learning to improve Bayesian network inference.
problem Bayesian network inference's reliance on specific algorithms can obscure causal relationships.
method Developed an ensemble learning approach to marginalize algorithm impact.
result Improved accuracy and reliability of causal network predictions.
Improved Bayesian neural network inference by selectively removing redundant modes.
problem Redundant modes in Bayesian neural network posteriors complicate approximate inference.
method Structured partial stochasticity and deterministic subset selection of weights.
result Improved performance of approximate inference schemes with simplified posterior distribution.
HuMaINs combines human and machine strengths for better inference tasks.
problem Improving inference performance through human-machine collaboration.
method Novel signal processing and machine learning solutions for HuMaINs architecture.
result HuMaINs achieves higher performance than either humans or machines individually.
New method provides exact performance guarantees for MAP inference in deep networks.
problem Analyzing MAP inference in deep networks with complex data.
method Multi-layer vector approximate message passing (ML-VAMP) method.
result Mean squared error of ML-VAMP estimate can be exactly characterized in high-dimensional random limit.
Meta-learn Bayesian inference for task-specific BNNs using amortised inference.
problem Efficiently learning Bayesian inference for small-scale probabilistic meta-learning.
method Replace global inducing points with actual data to create a set of approximate likelihoods, train a meta-model to learn these parameters across related datasets.
result Meta-learned inference can be applied to task-specific BNNs, improving efficiency and scalability.
Study on inferring dynamic communities and links in networks with memory.
problem Inferring communities and links in dynamic networks with memory.
method Maximum likelihood inference from single snapshot observations, analytical and numerical analysis.
result Link persistence makes community detection harder, while community persistence makes it easier.
Bayesian neural networks improve simulation-based inference with limited data.
problem Inaccurate inference in data-poor regimes with limited or expensive simulations.
method Bayesian neural networks for posterior approximation, accounting for computational uncertainty.
result Bayesian neural networks produce well-calibrated posteriors with few simulations.
A new inference network learns undirected models from data.
problem Learning undirected models with hidden variables is hard.
method Train a single stochastic inference network from data, using a cost function over queries.
result The network can perform inference tasks like an undirected model without partition function.
This paper improves deep learning by integrating Bayesian inference into network structure learning.
problem Bayesian inference in high-dimensional, over-parameterized neural networks.
method Developed an efficient stochastic variational inference approach to learn both network structure and weights.
result Empirically, the method exhibits competitive predictive performance and preserves Bayesian benefits.
This work develops algorithms to infer network structure and dynamics from partial nodal observations.
problem Inferring network structure and dynamics from limited nodal observations.
method Develops algorithms for joint inference of network topology and processes using structural equation models and structural vector autoregressive models.
result Effective algorithms for joint inference of network topology and processes from partial nodal observations, even in time-evolving networks.
Two new methods for variational inference without tractable densities.
problem Challenges in variational inference due to computationally intractable probability density functions.
method Introduces wild variational inference methods that do not require tractable density functions.
result Significant improvement in stochastic gradient Langevin dynamics (SGLD) step size adjustment.
NVGD uses neural networks to infer distributions without kernel choices.
problem Challenges in choosing kernel functions for SVGD.
method NVGD parameterizes the witness function of the Stein discrepancy with a neural network.
result NVGD achieves good performance on various inference problems.
Proposes a new method for continual learning in neural networks.
problem Challenges in applying sequential Bayesian inference to neural networks.
method Sequential function-space variational inference.
result Neural networks trained with the proposed method achieve better predictive accuracy.
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to be inferred, and its hyper-parameters are determined by cross validation. Then we formulate network inference as a matrix completion problem…
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.
A model infers dynamic networks from partial observation of cascading processes.
problem Inferring evolving networks from partial observation of node and edge data.
method A novel framework based on a mixture of coupled hierarchical Dirichlet processes.
result Explicit predictive distribution over edges of the underlying network, including future edges.
Hybrid approach combines VI and HMC for efficient Bayesian inference in neural networks.
problem Computational demands and inaccuracies in Bayesian inference for neural networks.
method Combines VI and HMC, reducing parameter space and accelerating inference.
result Significantly reduces inference time for large neural networks, improving uncertainty quantification.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well. We propose a fast non-iterative approximate inference metho…
The paper proposes a method for better uncertainty estimation in neural networks.
problem Estimating predictive uncertainty in neural networks is crucial but challenging.
method The paper proposes a function-space variational inference method to infer a posterior distribution over functions.
result The proposed method leads to state-of-the-art uncertainty estimation and predictive performance.
JADAI optimizes design and inference for parameter estimation.
problem Parameter estimation with active optimization of design variables.
method Jointly trains a policy, history network, and inference network to minimize posterior error.
result Achieves superior or competitive performance across benchmarks.
Scalable training of inference networks for Gaussian-process models.
problem Computational challenges in inference for large data in Gaussian process models.
method Stochastic inference networks with a functional mirror-descent algorithm for minibatch training.
result Empirical results show comparable and sometimes superior performance to existing sparse variational GP methods.
New algorithm accurately infers hierarchical network clusters.
problem Statistical inference of complex network structures.
method Scalable and reliable algorithm for hierarchical stochastic block models.
result Inferred models are more accurate than other scalable methods.
Bayesian inference with deep, weakly nonlinear networks is solved rigorously.
problem Bayesian inference with neural networks of specific structure.
method Perturbative analysis of fully connected neural networks with a shaped nonlinearity.
result Neural network Bayesian inference can be equivalent to kernel methods under certain conditions.
Sum-Product Networks (SPN) have recently emerged as a new class of tractable probabilistic graphical models. Unlike Bayesian networks and Markov networks where inference may be exponential in the size of the network, inference in SPNs is in time linear in the size of the network. Since SPNs represent distributions over…
New method learns cell trajectories and network interactions from single-cell data.
problem Network inference in systems biology from steady-state data.
method Min-entropy estimation for stochastic dynamics, leveraging both temporal and perturbational data.
result Jointly learns cellular trajectories and network interactions.
New method uses EKI for efficient Bayesian inference in high-dimensional problems.
problem Efficient inference for high-dimensional posterior distributions in physics-informed neural networks.
method Ensemble Kalman Inversion (EKI) for high-dimensional posterior inference.
result EKI-based inference provides comparable uncertainty estimates to HMC-based methods but with reduced computational cost.
A new framework for efficient Bayesian network inference.
problem High-dimensional Bayesian networks are hard to infer due to computational scaling.
method Directed convex subgraphs and minimal d-decomposition tree for decomposition, enabling parallel computation.
result The method reduces computational cost and enables parallel computation.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
problem Challenges of leveraging machine learning in highly distributed wireless networks.
method Developed a learning algorithm and architecture for distributed training and inference.
result Inference propagates and fuses across a network, with benefits over state-of-the-art techniques.
Bayesian inference improves neural network pruning efficiency.
problem Reducing computational and memory demands of large neural networks.
method Utilizes Bayesian inference to calculate Bayes factors for iterative pruning.
result Achieves desired levels of sparsity while maintaining competitive accuracy.