What Machine Learning Tells Us About the Mathematical Structure of
Concepts

What Machine Learning Tells Us About the Mathematical Structure of Concepts

28 August 2024

Jun Otsuka

Papers citing "What Machine Learning Tells Us About the Mathematical Structure of Concepts"

8 / 8 papers shown

Title
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings S. Dasgupta Michael Boratko Siddhartha Mishra Shriya Atmakuri Dhruvesh Patel Xiang Lorraine Li Andrew McCallum NAI 49 21 0 28 Jun 2021
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,837 0 06 May 2019
Towards a Definition of Disentangled Representations I. Higgins David Amos David Pfau S. Racanière Loic Matthey Danilo Jimenez Rezende Alexander Lerchner OCL DRL 95 479 0 05 Dec 2018
Poincaré Embeddings for Learning Hierarchical Representations Maximilian Nickel Douwe Kiela 85 1,304 0 22 May 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 178 3,690 0 10 Jun 2016
Group Equivariant Convolutional Networks Taco S. Cohen Max Welling BDL 160 1,934 0 24 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
Efficient Estimation of Word Representations in Vector Space Tomas Mikolov Kai Chen G. Corrado J. Dean 3DV 660 31,490 0 16 Jan 2013