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M. Opper

21 papers hereh-index 419.9k citations224 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author16

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • stat.ML9
  • cond-mat.dis-nn3
  • cs.IT2
  • cs.LG2
  • q-bio.NC2
  • cond-mat.stat-mech1
same name
  • M. Opper — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedInteracting particle solutions of Fokker-Planck equations through gradient-log-density estimation

58 citations · 136 across the 13 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

stat.ML2017★ 7 cited

Perturbative Black Box Variational Inference

Robert Bamler, Cheng Zhang, Manfred Opper +1

Black box variational inference (BBVI) with reparameterization gradients triggered the exploration of divergence measures other than the Kullback-Leibler (KL) divergence, such as a…

stat.ML2017★ 22 cited

Inverse Ising problem in continuous time: A latent variable approach

Christian Donner, Manfred Opper

We consider the inverse Ising problem, i.e. the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two…

cond-mat.dis-nn2017★ 11 cited

A statistical physics approach to learning curves for the Inverse Ising problem

Ludovica Bachschmid-Romano, Manfred Opper

Using methods of statistical physics, we analyse the error of learning couplings in large Ising models from independent data (the inverse Ising problem). We concentrate on learning…

cs.IT2017

Dynamical Functional Theory for Compressed Sensing

Burak Çakmak, Manfred Opper, Ole Winther +1

We introduce a theoretical approach for designing generalizations of the approximate message passing (AMP) algorithm for compressed sensing which are valid for large observation ma…

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