Researchers are exploring how to recover temporal structure from neural network weights, even after training is complete. One study proposes using hidden Markov models to analyze weight trajectories and identify distinct data regimes, showing improved generalization when data falls within the same identified state. Another investigation compares backpropagation with Hebbian learning, revealing that while both methods can train networks to perform tasks, Hebbian learning leads to significant representational drift in internal network states even after performance plateaus, unlike backpropagation. AI
IMPACT Investigating representational drift and weight analysis in neural networks could lead to better understanding of model behavior and improved generalization capabilities.
RANK_REASON The cluster contains two academic papers discussing research into neural network internal states and weight analysis.
- arXiv
- Fakeddit
- hidden Markov model
- Neural Networks
- Yelp
- backpropagation
- CNN
- Hebbian Learning
- Kolmogorov--Arnold Networks
- multilayer perceptron
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