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Found 3 papers, 1 papers with code
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
Taken together, our benchmarks establish a quantitative way to directly compare learning between neural networks models and human learners, show how choices in the mechanism by which such algorithms handle sample comparison and memory strongly impact their ability to match human learning abilities, and expose an open problem space for identifying more flexible and robust visual self-supervision algorithms.
Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion
In this work we explore the limiting dynamics of deep neural networks trained with stochastic gradient descent (SGD).
Symmetry, Conservation Laws, and Learning Dynamics in Neural Networks
Overall, by exploiting symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset.
