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Found 3 papers, 2 papers with code
Deep Learning Through the Lens of Example Difficulty
Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers.
What Do Neural Networks Learn When Trained With Random Labels?
We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.
Bayesian Neural Networks at Finite Temperature
We recapitulate the Bayesian formulation of neural network based classifiers and show that, while sampling from the posterior does indeed lead to better generalisation than is obtained by standard optimisation of the cost function, even better performance can in general be achieved by sampling finite temperature ($T$) distributions derived from the posterior.
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