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Found 5 papers, 2 papers with code
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning.
Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction
In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations.
Learning Temporally Causal Latent Processes from General Temporal Data
In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.
Learning Temporally Latent Causal Processes from General Temporal Data
Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.
