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Found 7 papers, 3 papers with code
Polarity is all you need to learn and transfer faster
Natural intelligences (NIs) thrive in a dynamic world - they learn quickly, sometimes with only a few samples.
Deep Discriminative to Kernel Generative Networks for Calibrated Inference
We leveraged the fact that deep models, including both random forests and deep networks, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as generalized partitioning rules.
Towards a theory of out-of-distribution learning
This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
