4 papers
Temperature is All You Need for Generalization in Langevin Dynamics and other Markov Processes
Itamar Harel, Yonathan Wolanowsky, Gal Vardi +2
We analyze the generalization gap (gap between the training and test errors) when training a potentially over-parametrized model using a Markovian stochastic training algorithm, in…
Provable Tempered Overfitting of Minimal Nets and Typical Nets
Itamar Harel, William M. Hoza, Gal Vardi +3
We study the overfitting behavior of fully connected deep Neural Networks (NNs) with binary weights fitted to perfectly classify a noisy training set. We consider interpolation usi…
Wetting Transition on Trees I: Percolation With Clustering
Aser Cortines, Itamar Harel, Dmitry Ioffe +1
A new ``Percolation with Clustering'' (PWC) model is introduced, where (the probabilities of) site percolation configurations on the leaf set of a binary tree are rewarded exponent…
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson +3
Background. A main theoretical puzzle is why over-parameterized Neural Networks (NNs) generalize well when trained to zero loss (i.e., so they interpolate the data). Usually, the N…