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Found 6 papers, 2 papers with code
Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models
We demonstrate that ComFe obtains higher accuracy compared to previous interpretable models across a range of fine-grained vision benchmarks, without the need to individually tune hyper-parameters for each dataset.
Efficient Out-of-Distribution Detection with Prototypical Semi-Supervised Learning and Foundation Models
This paper describes PAWS-VMK, an improved approach to prototypical semi-supervised learning in the field of computer vision, specifically designed to utilize a frozen foundation model as the neural network backbone.
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.
