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Found 10 papers, 3 papers with code
LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models
In this work, we present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models.
Towards Causal Representations of Climate Model Data
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios.
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders.
CLEAR: Causal Explanations from Attention in Neural Recommenders
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders.
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
Essentially, we tie the size of the CI conditioning set to its distance on the graph from the tested nodes, and increase this value in the successive iteration.
Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper
In this paper, we introduce such a strategy in the form of a recursive wrapper for existing constraint-based causal discovery algorithms, which preserves soundness and completeness.
A Single Iterative Step for Anytime Causal Discovery
We present a sound and complete algorithm for recovering causal graphs from observed, non-interventional data, in the possible presence of latent confounders and selection bias.
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
This approach leads to a new deep architecture, where networks are sampled from the posterior of local causal structures, and coupled into a compact hierarchy.
Bayesian Structure Learning by Recursive Bootstrap
The proposed method deals with the main weakness of constraint-based learning---sensitivity to errors in the independence tests---by a novel way of combining bootstrap with constraint-based learning.
Constructing Deep Neural Networks by Bayesian Network Structure Learning
We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph.
