ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1905.06401
  4. Cited By
Correlating neural and symbolic representations of language

Correlating neural and symbolic representations of language

14 May 2019
Grzegorz Chrupała
A. Alishahi
    NAI
ArXivPDFHTML

Papers citing "Correlating neural and symbolic representations of language"

11 / 11 papers shown
Title
Do Large Language Models Mirror Cognitive Language Processing?
Do Large Language Models Mirror Cognitive Language Processing?
Yuqi Ren
Renren Jin
Tongxuan Zhang
Deyi Xiong
50
4
0
28 Feb 2024
Towards Efficient Fine-tuning of Pre-trained Code Models: An
  Experimental Study and Beyond
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Ensheng Shi
Yanlin Wang
Hongyu Zhang
Lun Du
Shi Han
Dongmei Zhang
Hongbin Sun
33
42
0
11 Apr 2023
Unit Testing for Concepts in Neural Networks
Unit Testing for Concepts in Neural Networks
Charles Lovering
Ellie Pavlick
25
28
0
28 Jul 2022
How Familiar Does That Sound? Cross-Lingual Representational Similarity
  Analysis of Acoustic Word Embeddings
How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings
Badr M. Abdullah
Iuliia Zaitova
T. Avgustinova
Bernd Möbius
Dietrich Klakow
37
10
0
21 Sep 2021
How Does Adversarial Fine-Tuning Benefit BERT?
How Does Adversarial Fine-Tuning Benefit BERT?
J. Ebrahimi
Hao Yang
Wei Zhang
AAML
26
4
0
31 Aug 2021
Local Structure Matters Most: Perturbation Study in NLU
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouâtre
Prasanna Parthasarathi
Amal Zouaq
Sarath Chandar
30
13
0
29 Jul 2021
A Closer Look at How Fine-tuning Changes BERT
A Closer Look at How Fine-tuning Changes BERT
Yichu Zhou
Vivek Srikumar
26
63
0
27 Jun 2021
A comparative evaluation and analysis of three generations of
  Distributional Semantic Models
A comparative evaluation and analysis of three generations of Distributional Semantic Models
Alessandro Lenci
Magnus Sahlgren
Patrick Jeuniaux
Amaru Cuba Gyllensten
Martina Miliani
26
50
0
20 May 2021
Probing Classifiers: Promises, Shortcomings, and Advances
Probing Classifiers: Promises, Shortcomings, and Advances
Yonatan Belinkov
226
405
0
24 Feb 2021
Discovering the Compositional Structure of Vector Representations with
  Role Learning Networks
Discovering the Compositional Structure of Vector Representations with Role Learning Networks
Paul Soulos
R. Thomas McCoy
Tal Linzen
P. Smolensky
CoGe
29
43
0
21 Oct 2019
What you can cram into a single vector: Probing sentence embeddings for
  linguistic properties
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau
Germán Kruszewski
Guillaume Lample
Loïc Barrault
Marco Baroni
201
882
0
03 May 2018
1