A new research paper published on arXiv explores phase transitions in learning, specifically within extremal regression problems that are a type of multiple instance learning (MIL). The study focuses on how max pooling, a common technique in MIL, can lead to vanishing gradients. To address this, researchers propose approximating the max function with a softmax, where the sharpness is a learnable parameter. The paper derives a projection of the gradient flow in a minimal model, revealing two distinct phase transitions: an intrinsic freezing transition and a retrieval transition, which are confirmed through numerical simulations. AI
IMPACT This research could lead to improved understanding and handling of vanishing gradients in specific machine learning tasks.
RANK_REASON The cluster contains a research paper detailing theoretical findings in machine learning.
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- arXiv
- Extremal Regression
- gradient descent
- Hopfield Networks
- Hugging Face
- Max pooling processor based on 1T1R memory
- Multiple instance learning
- Random energy model
- Softmax
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