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Found 4 papers, 1 papers with code
Determining the Relevance of Features for Deep Neural Networks
Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to understand black-box classifiers or predictors.
Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests
Phenological dates, such as the start and end of the growing season, are critical for understanding the exchange of carbon and water between the biosphere and the atmosphere.
Learning Disentangled Discrete Representations
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear.
Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing
Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones.
