1 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery
Yuzhou Cheng, Tom Yates, Ignacio Alzugaray +3
Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spatial intelligence. However, we…
Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
Jatin Sharma, Dan F. M Goodman, Danyal Akarca
Understanding how biological and artificial neural networks implement computation from connectivity is a central problem in neuroscience and machine learning. In neural systems, st…
The Principle of Maximum Heterogeneity Optimises Productivity in Distributed Production Systems Across Biology, Economics, and Computing
Guillhem Artis, Danyal Akarca, Jascha Achterberg
The world is full of systems of distributed agents, collaborating and competing in complex ways: firms and workers specialise within economies, neurons adapt their tuning across br…
Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs
Tom Yates, Yuzhou Cheng, Ignacio Alzugaray +3
Belief Propagation (BP) is a powerful algorithm for distributed inference in probabilistic graphical models, however it quickly becomes infeasible for practical compute and memory…