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Found 7 papers, 4 papers with code
Training objective drives the consistency of representational similarity across datasets
Moreover, the correspondence between representational similarities and the models' task behavior is dataset-dependent, being most strongly pronounced for single-domain datasets.
Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems.
An Analysis of Human Alignment of Latent Diffusion Models
Diffusion models, trained on large amounts of data, showed remarkable performance for image synthesis.
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected.
Human alignment of neural network representations
Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.
