Proposes a method to propagate uncertainty through tanh activation in reservoir computing.
problem Computing output distribution for neural networks with tanh activation when given a probability distribution.
method Moment-based approach to propagate uncertainty through Echo State Network (ESN).
result Probabilistic Echo State Network (PESN) shows better performance than deterministic ESNs.
Geometric approach to Dirac operator evolution on spacetimes.
problem Constructing the Cauchy evolution operator for Lorentzian Dirac operators.
method Realizing the operator as a sum of oscillatory integrals, relating to Feynman propagator.
result Relating Cauchy evolution operators to Feynman propagators and constructing Hadamard states.
Paper introduces MNNs for efficient neural network training.
problem Training large-scale sparse neural networks efficiently.
method Formal derivation of MNN architecture with forward-only gradient propagation.
result MNNs can efficiently compute gradients without backward computation.
The method infers Markov process parameters from steady state snapshots.
problem Inferring parameters from non-equilibrium steady states without Boltzmann distribution.
method Propagator likelihood based on fictitious transitions.
result Efficient reconstruction of parameters in various systems.
The article constructs Feynman propagators for normally hyperbolic operators on curved spacetimes.
problem Constructing Feynman propagators for non-scalar geometric operators on curved spacetimes.
method Global microlocalisation constructions for normally hyperbolic operators on globally hyperbolic spacetimes.
result Feynman propagators can be constructed to satisfy a positivity property for selfadjoint normally hyperbolic operators.
Symmetries in shrinking Ricci solitons spread outward.
problem Understanding symmetries in shrinking Ricci solitons.
method Propagating approximate symmetries to larger scales.
result Symmetries in shrinking Ricci solitons spread outward.
We construct default-free interest rate models in the spirit of the well-known Markov funcional models: our focus is analytic tractability of the models and generality of the approach. We work in the setting of state price densities and construct models by means of the so called propagation property. The propagation pr…
GP-SUM filters complex non-Gaussian states using Gaussian Processes.
problem Stochastic dynamic filtering and state propagation with complex beliefs.
method GP-SUM combines sampling and probabilistic Bayes filters, using Gaussian Processes for dynamic and observation models.
result GP-SUM outperforms other filters on benchmarks and predicts non-Gaussian states accurately.
New dissimilarity measures enhance affinity propagation for complex network clustering.
problem Improving community detection in complex networks using affinity propagation.
method Leverage network latent geometry to design dissimilarity matrices.
result Affinity propagation outperforms state-of-the-art methods in community detection.
A new method backtracks through a few key past states to speed up credit assignment in long sequences.
problem Computational inefficiency of back-propagation through time for long sequences.
method Sparse attentive backtracking using learned attention mechanisms to skip connections.
result Matches or outperforms regular BPTT and truncated BPTT in tasks with long-term dependencies.
EBU improves deep RL with episodic backward updates, achieving human-level performance with less data.
problem Efficiently learn from sparse and delayed rewards in reinforcement learning.
method Episodic Backward Update (EBU) algorithm that recursively propagates value updates.
result EBU achieves human-level performance in Atari games with significantly fewer samples.
A new method combines topological features with graph convolutional networks for improved paper classification.
problem Classifying papers based on their content and structure.
method Combining topological features of nodes with information propagation through Graph Convolutional Networks (GCN).
result The method improves classification accuracy on CiteSeer and Cora datasets, matching or exceeding text-based classification results.
Unified model combines feature and label propagation for semi-supervised classification.
problem Combining feature and label propagation for effective semi-supervised classification.
method Unified Message Passing Model (UniMP) using Graph Transformer and masked label prediction.
result Obtains new state-of-the-art results in Open Graph Benchmark (OGB).
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capt…
Belief Propagation outperforms other algorithms in reconstructing binary symmetric channel trees.
problem Reconstructing binary symmetric channel trees with bounded memory.
method Combining recursive reconstruction, information theory, and optimal transport.
result Any recursive algorithm with bounded memory for the reconstruction problem on binary symmetric channel trees has a phase transition strictly below the Belief Propagation threshold.
Deep learning method for 3D cardiac segmentation with spatial propagation.
problem Cardiac segmentation from MRI stacks with spatial consistency.
method Iterative deep learning with U-net for spatial propagation, training on UK Biobank.
result Comparable or better results than state-of-the-art, enhanced spatial consistency.
Enhances belief propagation to find global optima without increasing computational burden.
problem Improving probabilistic inference accuracy on graphical models.
method Homotopy continuation method that gradually incorporates pairwise potentials.
result SBP finds the global optimum of the Bethe approximation for attractive models.
Quantum propagation studied for Berezin-Toeplitz operators.
problem Asymptotic behavior of quantum propagators and spectral projectors.
method Geometric analysis of Hamiltonian flows and Maslov indices.
result Introduction of quantum states associated with Lagrangian submanifolds.
Interval attacks find more adversarial examples than existing methods.
problem Evaluating robustness of adversarially trained neural networks against unknown attacks.
method Symbolic interval propagation for bound over-approximation and gradient-guided attacks.
result Interval attacks find on average 47% more violations than state-of-the-art methods.
Paper learns dynamic generator models for video sequences.
problem Modeling spatial-temporal processes like dynamic textures and actions.
method Alternating back-propagation through time algorithm to learn latent state vectors and generator model.
result Trains realistic models for dynamic textures and actions.
Binary classification models get more efficient predictive probabilities.
problem Computing predictive probabilities in Bayesian probit models is computationally challenging.
method Use of expectation propagation (EP) to find a closed-form expression for predictive probabilities.
result Closed-form predictive probabilities improve over existing methods.
A new method improves label propagation for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation through semi-supervised learning techniques.
method Label Propagation with Augmented Anchors (A2LP) for UDA. result A2LP improves over representative UDA methods and benchmarks. Detects rumours using news propagation patterns and user interactions.
problem Rumours' negative impact on social media platforms.
method Deep learning approach that learns user representations and temporal interactions.
result State-of-the-art performance in rumour detection on Twitter and Weibo datasets.
A new method for inferring latent states in Markov jump processes.
problem Intractable exact inference for Markov jump processes.
method Entropic matching within expectation propagation.
result Superior performance in approximating the mean of the posterior process.
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
problem Challenges in MIMO detection due to interference and noise in high-order high-dimensional systems.
method The approach uses a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and Expectation Propagation (EP) messages to improve detection accuracy.
result The proposed algorithm outperforms state-of-the-art detection algorithms while maintaining low computational complexity.
Efficient EP algorithm improves smoothing distribution inference in financial models.
problem Computational intractability of smoothing distribution in high dimensions.
method Adapted expectation propagation (EP) algorithms for the unified skew-normal family.
result Accuracy gains in financial illustrations over existing approximate algorithms.
GEnBP combines EnKF and GaBP for efficient high-dimensional inference.
problem Efficient inference in high-dimensional models.
method Gaussian Ensemble Belief Propagation algorithm combining EnKF and GaBP.
result GEnBP outperforms existing methods in accuracy and efficiency.
Improved Gaussian Process model for predicting trajectories without independence assumption errors.
problem Incorrect independence assumption in previous work on Gaussian Process uncertainty propagation.
method Proposed a novel piecewise linear approximation to correct the independence assumption in continuous models.
result Corrected the independence assumption in Gaussian Process models for predicting trajectories.
This work improves LSTM and GRU training stability and generalization.
problem Training instabilities in LSTMs and GRUs on long sequences.
method Developed a mean field theory to optimize initialization hyperparameters.
result Eliminates or reduces training instabilities and improves generalization.
TPN learns to propagate labels for few-shot learning.
problem Few-shot learning with limited training instances per class.
method Transductive Propagation Network (TPN) learns to propagate labels from labeled to unlabeled instances.
result TPN achieves state-of-the-art results on multiple benchmark datasets.
New method improves ResNet performance without batch normalization.
problem Improving ResNet performance without batch normalization.
method Adapted Weight Standardization to maintain signal propagation.
result Highly performant ResNets achieve state-of-the-art performance on ImageNet.
The paper proves regularity of states on manifolds with unstable dynamics.
problem Propagation of regularity in dynamical systems with unstable manifolds.
method Leafwise semiclassical pseudodifferential calculus adapted to foliated spaces.
result Pollicott-Ruelle resonant states are smooth over entire manifolds if smooth on unstable leaves.
Paper introduces a new network learning method with local propagation.
problem Learning in complex, multi-path networks with multiple goals.
method Locally decoupled network parameter learning with local propagation.
result Advantages in learning time and network size compared to state-of-the-art methods.
We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a continuous state space. The algorithm constructs adaptively efficient proposal distributions approximating the local beliefs at each note of the MRF. This is achieved by cons…
MambaLRP enhances Mamba models' explainability and performance.
problem Lack of transparency in Mamba models for real-world applications.
method Layer-wise Relevance Propagation (LRP) with relevance conservation axioms.
result MambaLRP provides stable and reliable explanations for Mamba models.
BPNNs learn to solve combinatorial problems faster and more accurately.
problem Generalizing belief propagation for efficient problem solving.
method BPNNs are parameterized operators that operate on factor graphs, generalizing BP. BPNN-D is a learned iterative operator that provably maintains BP's properties.
result BPNN-D converges 1.7x faster on Ising models and provides tighter bounds.
Generalizes neural network verification by adding arbitrary cutting planes.
problem Handling general cutting plane constraints in neural network verification.
method Generalized bound propagation method (GCP-CROWN) that allows arbitrary cutting plane constraints.
result GCP-CROWN significantly improves neural network verification performance.
Novel methods for splitting Gaussian mixtures improve uncertainty propagation in nonlinear systems.
problem Improving accuracy and efficiency in nonlinear uncertainty propagation.
method Preserving mean and covariance, novel heuristics for selecting splitting direction informed by initial uncertainty and nonlinear function properties.
result Improved accuracy and efficiency in uncertainty propagation compared to existing techniques.
A neural network model minimizes region-based free energy for faster inference in MRFs.
problem Efficient inference in complex Markov random fields (MRFs).
method Region-based Energy Neural Network (RENN) that directly minimizes region-based free energy.
result RENN outperforms other methods in marginal distribution estimation, partition function estimation, and MRF learning.
TD learning with neural networks can lead to worse solutions than Monte-Carlo methods, especially in discontinuous value functions.
problem TD learning with neural networks can propagate approximation errors, leading to worse solutions than Monte-Carlo methods.
method Investigated the issue of approximation errors in areas of sharp discontinuities of the value function being further propagated by bootstrap updates.
result Empirical and analytical evidence shows that leakage propagation occurs in TD learning with function approximation, especially in sharp discontinuities.
FlowGN tackles graph representation learning by tracing information flow paths.
problem GCNs struggle with over-smoothing and scalability issues.
method FlowGN introduces a 'SourceoSink' mode and 'information flow path' concept. result FlowGN outperforms state-of-the-art GCNs in public datasets.
GPNNs improve semi-supervised classification on large graphs.
problem Handling large graphs for semi-supervised classification.
method Alternates local and global graph propagation with partitioning.
result GPNNs achieve similar performance with fewer steps than standard GNNs.
New method uncovers small but significant local activities in time-series data.
problem Reconstructing small but important local activities in time-series data.
method Neural state-space models with latent causal-effect disentanglement.
result Demonstrated proof-of-concept on reconstructing ectopic foci in cardiac electrical propagation.
Unified framework improves gene prioritization in disease studies.
problem Identifying genes involved in diseases using heterogeneous biological data.
method Network propagation-based gene prioritization with integrated biological information.
result Significant improvements in prioritizing genes not identified by traditional methods.
Improved video prediction with bijective Gated Recurrent Units.
problem Ill-posed future video prediction with high variability and error propagation.
method Introduces bijective Gated Recurrent Units for state sharing in auto-encoders.
result Significant reduction in computational cost and memory usage compared to state-of-the-art approaches.
Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including bi…
New approach learns neural network parameters by balancing local objectives and data propagation constraints.
problem Learning neural network parameters with trade-offs between local objectives and data propagation.
method Introduces nonlinear transforms and local propagation constraints to balance learning objectives.
result Validated approach on image recognition tasks with improved learning time and network size.
Unified framework for efficient Gaussian process inference.
problem Efficient inference in non-conjugate Gaussian process models.
method Combines expectation propagation with linearization for improved efficiency.
result Unified view of various inference schemes, including classical smoothers and EP.