 [2402.12397] Multi-class Temporal Logic Neural Networks






























  











Grab your spot!
Want to see access to research regardless of disability? Sign up for the arXiv Accessibility Forum in September and Learn more.


Sign Up





Skip to main content





Grab your spot at the free arXiv Accessibility Forum
Forum Schedule

We gratefully acknowledge support fromthe Simons Foundation, Stockholm University, and all contributors. Donate





 > stat > arXiv:2402.12397
  





Help | Advanced Search




All fields
Title
Author
Abstract
Comments
Journal reference
ACM classification
MSC classification
Report number
arXiv identifier
DOI
ORCID
arXiv author ID
Help pages
Full text




Search















open search






GO



open navigation menu


quick links

Login
Help Pages
About












Statistics > Machine Learning


arXiv:2402.12397 (stat)
    




  [Submitted on 17 Feb 2024 (v1), last revised 25 Jun 2024 (this version, v2)]
Title:Multi-class Temporal Logic Neural Networks
Authors:Danyang Li, Roberto Tron View a PDF of the paper titled Multi-class Temporal Logic Neural Networks, by Danyang Li and 1 other authors
View PDF
HTML (experimental)

Abstract:Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. The task of binary and multi-class classification for time-series data has become a prominent area of research. Neural networks represent a popular approach to classifying data; However, they lack interpretability, which poses a significant challenge in extracting meaningful information from them. Signal Temporal Logic (STL) is a formalism that describes the properties of timed behaviors. We propose a method that combines all of the above: neural networks that represent STL specifications for multi-class classification of time-series data. We offer two key contributions: 1) We introduce a notion of margin for multi-class classification, and 2) we introduce STL-based attributes for enhancing the interpretability of the results. We evaluate our method on two datasets and compare it with state-of-the-art baselines.
    



Subjects:

Machine Learning (stat.ML); Machine Learning (cs.LG)

Cite as:
arXiv:2402.12397 [stat.ML]


 
(or 
arXiv:2402.12397v2 [stat.ML] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2402.12397



Focus to learn more




                arXiv-issued DOI via DataCite
              







Submission history From: Danyang Li [view email]       [v1]
        Sat, 17 Feb 2024 00:22:29 UTC (281 KB)
[v2]
        Tue, 25 Jun 2024 02:58:06 UTC (297 KB)



 

Full-text links:
Access Paper:


View a PDF of the paper titled Multi-class Temporal Logic Neural Networks, by Danyang Li and 1 other authorsView PDFHTML (experimental)TeX SourceOther Formats
view license

 
    Current browse context: stat.ML


< prev

  |   
next >


new
 | 
recent
 | 2024-02

    Change to browse by:
    
cs
cs.LG
stat




References & Citations

NASA ADSGoogle Scholar
Semantic Scholar




a
export BibTeX citation
Loading...




BibTeX formatted citation
×


loading...


Data provided by: 




Bookmark





 




Bibliographic Tools

Bibliographic and Citation Tools






Bibliographic Explorer Toggle



Bibliographic Explorer (What is the Explorer?)







Litmaps Toggle



Litmaps (What is Litmaps?)







scite.ai Toggle



scite Smart Citations (What are Smart Citations?)








Code, Data, Media

Code, Data and Media Associated with this Article






Links to Code Toggle



CatalyzeX Code Finder for Papers (What is CatalyzeX?)







DagsHub Toggle



DagsHub (What is DagsHub?)







GotitPub Toggle



Gotit.pub (What is GotitPub?)







Links to Code Toggle



Papers with Code (What is Papers with Code?)







ScienceCast Toggle



ScienceCast (What is ScienceCast?)











Demos

Demos






Replicate Toggle



Replicate (What is Replicate?)







Spaces Toggle



Hugging Face Spaces (What is Spaces?)







Spaces Toggle



TXYZ.AI (What is TXYZ.AI?)








Related Papers

Recommenders and Search Tools






Link to Influence Flower



Influence Flower (What are Influence Flowers?)







Connected Papers Toggle



Connected Papers (What is Connected Papers?)







Core recommender toggle



CORE Recommender (What is CORE?)





Author
Venue
Institution
Topic














        About arXivLabs
      



arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.










Which authors of this paper are endorsers? |
    Disable MathJax (What is MathJax?)
    












About
Help





contact arXivClick here to contact arXiv
 Contact


subscribe to arXiv mailingsClick here to subscribe
 Subscribe











Copyright
Privacy Policy




Web Accessibility Assistance


arXiv Operational Status 
                    Get status notifications via
                    email
                    or slack





 





