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Found 10 papers, 6 papers with code
Designing Optimal Behavioral Experiments Using Machine Learning
As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model.
Domain Knowledge in A*-Based Causal Discovery
Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data.
Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and experimental design.
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
Bayesian Optimal Experimental Design for Simulator Models of Cognition
Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data.
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
We address this gap in the literature by devising a novel sequential design framework for parameter estimation that uses the Mutual Information (MI) between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models.
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
A fundamental question is how to design the experiments so that the collected data are most useful.
Efficient Bayesian Experimental Design for Implicit Models
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance.
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion
We present our system description of input-level multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language.
