Search Results for author:
Found 6 papers, 3 papers with code
ComFe: Interpretable Image Classifiers With Foundation Models, Transformers and Component Features
Interpretable computer vision models are able to explain their reasoning through comparing the distances between the image patch embeddings and prototypes within a latent space.
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection
This paper describes PAWS-VMK, a prototypical deep learning approach that obtains state-of-the-art results for image classification tasks in both a semi-supervised learning (SSL) and out-of-distribution (OOD) detection context.
Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
In this paper, we proposed a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression.
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations.
FedDAG: Federated DAG Structure Learning
To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server.
Variational approximations using Fisher divergence
Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated.
