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State of Machine Learning Theory | Report | ScienceToStartup

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Machine Learning Theory

State of Machine Learning Theory

4 papers · avg viability 3.0

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Top papers

Contrastive Conformal Sets(4.0)
Rod Flow: A Continuous-Time Model for Gradient Descent at the Edge of Stability(3.0)
Uncertainty propagation through trained multi-layer perceptrons: Exact analytical results(3.0)
Principles of Lipschitz continuity in neural networks(2.0)
Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels(0.0)
Towards Initialization-dependent and Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks(0.0)
Rapid mixing in positively weighted restricted Boltzmann machines(0.0)