NCoder—a quantum field theory approach to encoding data
In this paper we present a novel approach to interpretable AI inspired by quantum field theory which we call the NCoder . The NCoder is a modified autoencoder neural network whose latent layer is prescribed to be a subset of n -point correlation functions. Regarding images as draws from a lattice fi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ade04c |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850163333387255808 |
|---|---|
| author | D S Berman M S Klinger A G Stapleton |
| author_facet | D S Berman M S Klinger A G Stapleton |
| author_sort | D S Berman |
| collection | DOAJ |
| description | In this paper we present a novel approach to interpretable AI inspired by quantum field theory which we call the NCoder . The NCoder is a modified autoencoder neural network whose latent layer is prescribed to be a subset of n -point correlation functions. Regarding images as draws from a lattice field theory, this architecture mimics the task of perturbatively constructing the effective action of the theory order by order in an expansion using Feynman diagrams. Alternatively, the NCoder may be regarded as simulating the procedure of statistical inference whereby high dimensional data is first summarized in terms of several lower dimensional summary statistics (here the n -point correlation functions), and subsequent out-of-sample data is generated by inferring the data generating distribution from these statistics. In this way the NCoder suggests a fascinating correspondence between perturbative renormalizability and the sufficiency of models. We demonstrate the efficacy of the NCoder by applying it to the generation of MNIST images, and find that generated images can be correctly classified using only information from the first three n -point functions of the image distribution. |
| format | Article |
| id | doaj-art-513b293607df4f2a8ec41ec0b7c31daf |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-513b293607df4f2a8ec41ec0b7c31daf2025-08-20T02:22:19ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202505910.1088/2632-2153/ade04cNCoder—a quantum field theory approach to encoding dataD S Berman0https://orcid.org/0000-0002-5382-1668M S Klinger1https://orcid.org/0009-0001-7666-1185A G Stapleton2https://orcid.org/0009-0009-6784-7779Centre for Theoretical Physics, Queen Mary University of London , E1 4NS London, United KingdomDepartment of Physics, University of Illinois , Urbana, IL 61801, United States of AmericaCentre for Theoretical Physics, Queen Mary University of London , E1 4NS London, United KingdomIn this paper we present a novel approach to interpretable AI inspired by quantum field theory which we call the NCoder . The NCoder is a modified autoencoder neural network whose latent layer is prescribed to be a subset of n -point correlation functions. Regarding images as draws from a lattice field theory, this architecture mimics the task of perturbatively constructing the effective action of the theory order by order in an expansion using Feynman diagrams. Alternatively, the NCoder may be regarded as simulating the procedure of statistical inference whereby high dimensional data is first summarized in terms of several lower dimensional summary statistics (here the n -point correlation functions), and subsequent out-of-sample data is generated by inferring the data generating distribution from these statistics. In this way the NCoder suggests a fascinating correspondence between perturbative renormalizability and the sufficiency of models. We demonstrate the efficacy of the NCoder by applying it to the generation of MNIST images, and find that generated images can be correctly classified using only information from the first three n -point functions of the image distribution.https://doi.org/10.1088/2632-2153/ade04cneural networkcorrelation functionsquantum field theoryperturbation theoryedgeworthmoment problem |
| spellingShingle | D S Berman M S Klinger A G Stapleton NCoder—a quantum field theory approach to encoding data Machine Learning: Science and Technology neural network correlation functions quantum field theory perturbation theory edgeworth moment problem |
| title | NCoder—a quantum field theory approach to encoding data |
| title_full | NCoder—a quantum field theory approach to encoding data |
| title_fullStr | NCoder—a quantum field theory approach to encoding data |
| title_full_unstemmed | NCoder—a quantum field theory approach to encoding data |
| title_short | NCoder—a quantum field theory approach to encoding data |
| title_sort | ncoder a quantum field theory approach to encoding data |
| topic | neural network correlation functions quantum field theory perturbation theory edgeworth moment problem |
| url | https://doi.org/10.1088/2632-2153/ade04c |
| work_keys_str_mv | AT dsberman ncoderaquantumfieldtheoryapproachtoencodingdata AT msklinger ncoderaquantumfieldtheoryapproachtoencodingdata AT agstapleton ncoderaquantumfieldtheoryapproachtoencodingdata |