Exact learning dynamics of deep linear networks with prior knowledge.

Clémentine C J Dominé, Lukas Braun, James E Fitzgerald, Andrew M Saxe

Abstract read

In one paragraph

Article in Journal of statistical mechanics (Online), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron.Advances in neural information processing systems · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Clémentine C J DominéGatsby Computational Neuroscience Unit, University College London, London, United Kingdom.

Lukas BraunDepartment of Experimental Psychology, University of Oxford, Oxford, United Kingdom.

James E FitzgeraldHoward Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, VA, United States of America.

Andrew M SaxeGatsby Computational Neuroscience Unit, University College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Learning in deep neural networks is known to depend critically on the knowledge embedded in the initial network weights. However, few theoretical results have precisely linked prior knowledge to learning dynamics. Here we derive exact solutions to the dynamics of learning with rich prior knowledge in deep linear networks by generalising Fukumizu's matrix Riccati solution (Fukumizu 1998

Indexed as

deep learninglearning theorymachine learning

Identifiers

PMID38524253

DOI10.1088/1742-5468/ad01b8

PMCPMC10955603

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.