Search Results for author:
Found 7 papers, 3 papers with code
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Interpretability techniques are valuable for helping humans understand and oversee AI systems.
Learning Internal Representations of 3D Transformations from 2D Projected Inputs
When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images.
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning
Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning.
Learning Identity-Preserving Transformations on Data Manifolds
However, these approaches are limited because they require transformation labels when training their models and they lack a method for determining which regions of the manifold are appropriate for applying each specific operator.
Generative causal explanations of black-box classifiers
Our objective function encourages both the generative model to faithfully represent the data distribution and the latent factors to have a large causal influence on the classifier output.
Representing Closed Transformation Paths in Encoded Network Latent Space
Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space.
Transfer Learning on Manifolds via Learned Transport Operators
The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data.
