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Found 8 papers, 3 papers with code
FiP: a Fixed-Point Approach for Causal Generative Modeling
Based on this, we design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, thus by-passing the search, and then learns the generative fixed-point SCM on the ordered variables.
The Essential Role of Causality in Foundation World Models for Embodied AI
This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these.
Learned Causal Method Prediction
In this work, we propose CAusal Method Predictor (CAMP), a framework for predicting the best method for a given dataset.
Understanding Causality with Large Language Models: Feasibility and Opportunities
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
Scaling Federated Learning for Fine-tuning of Large Language Models
We perform an extensive sweep over the number of clients, ranging up to 32, to evaluate the impact of distributed compute on task performance in the federated averaging setting.
Perceiving Music Quality with GANs
By using the human rated dataset we show that the discriminator score correlates significantly with the subjective ratings, suggesting that the proposed method can be used to create a no-reference musical audio quality assessment measure.
Towards Machine Learning on data from Professional Cyclists
Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions.
