belief propagation
PulseAugur coverage of belief propagation — every cluster mentioning belief propagation across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
-
New method trains decoder ensembles for improved LDPC code reliability
Researchers have developed a novel method for training ensembles of weighted belief propagation decoders to improve the reliability of decoding low-density parity-check (LDPC) codes. Instead of relying on code-specific …
-
New research characterizes information-computation gap in sparse stochastic block models
Researchers have characterized the information-computation gap in sparse stochastic block models, focusing on community recovery with multiple communities. They found that at the Kesten-Stigum threshold ($d\[lambda\]^2=…
-
New theory explains cross-lingual similarity in language model representations
Researchers have developed a theoretical framework to explain the phenomenon of similar representations for translated sentences in the inner layers of multilingual language models. This framework is based on the idea t…
-
Qubit-centric Transformer advances quantum error correction
Researchers have developed a novel Qubit-centric Transformer (QCT) decoder for quantum error correction, leveraging deep learning and a transformer architecture with a specialized qubit-centric attention mechanism. This…
-
New ML decoder boosts quantum error correction accuracy
Researchers have developed a new machine learning decoder, called the Quantum Bayesian graph Attention decoder (QuBA), designed to improve the accuracy of quantum error correction. This decoder aims to provide reliable …
-
New Graph Transformer Improves Inference in Graphical Models
Researchers have developed In-Context Graphical Inference (ICG-I), a novel autoregressive Graph Transformer designed to improve marginal inference in discrete graphical models. This new method mimics the Variable Elimin…
-
New method enables constrained sequence generation with variable-order Markov models
Researchers have developed a new method for generating sequences using variable-order Markov models that incorporates regular constraints. This approach extends existing belief propagation techniques to handle complex r…
-
Graph Normalization offers differentiable approximation for NP-hard MWIS problem
Researchers have developed Graph Normalization (GN), a novel dynamical system that approximates the NP-hard Maximum Weight Independent Set (MWIS) problem. GN offers a principled and differentiable approach, converging t…