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Found 6 papers, 2 papers with code
Uncertainty in latent representations of variational autoencoders optimized for visual tasks
Deep learning methods are increasingly becoming instrumental as modeling tools in computational neuroscience, employing optimality principles to build bridges between neural responses and perception or behavior.
Unsupervised bias discovery in medical image segmentation
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity.
Towards unraveling calibration biases in medical image analysis
In recent years the development of artificial intelligence (AI) systems for automated medical image analysis has gained enormous momentum.
Domain Generalization via Gradient Surgery
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains.
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference
Theories for autism spectrum disorder (ASD) have been formulated at different levels: ranging from physiological observations to perceptual and behavioral descriptions.
Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
This self-limiting behavior allows for stable online learning for arbitrary durations.
