Paul Blomstedt

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Statistics and Probability — 19 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Optimization & Training — 5 citations into it

Whom Paul Blomstedt trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Tom Minka

Papers from Paul Blomstedt's perspective

Ordered by how strongly the researchers Paul Blomstedt trusts endorse each paper. Same corpus, a different order for every researcher.

1. Expectation Propagation for approximate Bayesian inference
2. Power EP
3. Large Scale Distributed Deep Networks
4. Black Box Variational Inference
5. Gaussian Processes for Classification: Mean-Field Algorithms
6. Sequential Monte Carlo samplers
7. Expectation-Propogation for the Generative Aspect Model (Conference on Uncertainty in Artificial Intelligence)
8. Hierarchical Variational Models
9. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
10. Automatic Differentiation Variational Inference

Their papers

• Expectation Propagation as a Way of Life (2020) — 26 citations
• Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data (2014) — 43 citations