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Reliability of CKA as a Similarity Measure in Deep Learning
Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different ways.
Exploring the Geometry and Topology of Neural Network Loss Landscapes
This suggests that qualitative and quantitative examination of the loss landscape geometry could yield insights about neural network generalization performance during training.
Advantages of biologically-inspired adaptive neural activation in RNNs during learning
Dynamic adaptation in single-neuron response plays a fundamental role in neural coding in biological neural networks.
Internal representation dynamics and geometry in recurrent neural networks
The efficiency of recurrent neural networks (RNNs) in dealing with sequential data has long been established.
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