A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved accuracy in community detection with vertex labels.
BBPL uses block updates to learn Markov random fields without full inference.
Inference of hidden classes in stochastic block model is a classical problem with important applications. Most commonly used methods for this problem involve naïve mean field approaches or heuristic spectral methods. Recently, belief propagation was proposed for this problem. In this contribution we perform a comparati…
Neural decoders improve performance on large algebraic block codes.
StreamBP optimally detects communities in growing networks.
Generalized belief propagation converges to optimal solutions on graphs with motifs.
Belief propagation recovers backpropagation results.
Improved error correction using neural networks and belief propagation.
This thesis investigates belief propagation's performance in graphical models with loops.
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical gu…
Optimal algorithms identified for semi-supervised classification on graphs.
Improved BP algorithm outperforms loopy BP in MAP inference.
New -BP algorithm improves belief propagation for graphs with loops.
New group testing method uses Belief Propagation for accurate screening.
There is a recent surge of interest in identifying the sharp recovery thresholds for cluster recovery under the stochastic block model. In this paper, we address the more refined question of how many vertices that will be misclassified on average. We consider the binary form of the stochastic block model, where ver…
Belief Propagation outperforms other algorithms in reconstructing binary symmetric channel trees.
SOLBP extends efficient inference to uncertain Bayesian networks.
Enhances belief propagation to find global optima without increasing computational burden.
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such marginals when the underlying graph is a tree. But it has gained its popularity as …
GEnBP combines EnKF and GaBP for efficient high-dimensional inference.
Belief propagation quickly converges to global optima for ferromagnetic Ising models.
This paper improves parallel belief propagation for scalable machine learning.
The paper uses belief propagation to analyze rankings and partial orders from partial information.
The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameter…
Improved text summarization using belief propagation on weighted bipartite graphs.
Efficiently learns deep factor graphs using Gaussian belief propagation.
Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on the …
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…
BPNNs learn to solve combinatorial problems faster and more accurately.
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, w…
New algorithm for efficient inference over tree-structured graphs.
Paper uses EXIT analysis for community detection with side information.
Two EP frameworks ensure integrable beliefs in Bayesian estimation problems.
We propose a feed-forward inference method applicable to belief and neural networks. In a belief network, the method estimates an approximate factorized posterior of all hidden units given the input. In neural networks the method propagates uncertainty of the input through all the layers. In neural networks with inject…
PGMax automates PGM inference on GPUs, improving quality and speed.
We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to station…
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms a…
The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatori…
A new algorithm estimates aggregate marginals from noisy data in an online manner.
Gaussian BP algorithm converges exponentially under walk summability for cyclic graphs.
MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence…
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti and M. Mézard [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for fin…
A neural network model minimizes region-based free energy for faster inference in MRFs.
Factor graphs are important models for succinctly representing probability distributions in machine learning, coding theory, and statistical physics. Several computational problems, such as computing marginals and partition functions, arise naturally when working with factor graphs. Belief propagation is a widely deplo…
Adversarial inference on tree models is possible with limited corruption, improving on Kesten-Stigum threshold.
The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks…
Paper compares hard and soft EM for BN learning from incomplete data.
NoProp learns neural networks without full back-propagation or forward-propagation.