59 citations · 85 across the 14 of their papers we have counts for
9 papers · 1 filter
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
Erik Englesson, Amir Mehrpanah, Hossein Azizpour
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters c…
On the Lipschitz Constant of Deep Networks and Double Descent
Matteo Gamba, Hossein Azizpour, Mårten Björkman
Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms c…
Deep Double Descent via Smooth Interpolation
Matteo Gamba, Erik Englesson, Mårten Björkman +1
The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms o…
Are All Linear Regions Created Equal?
Matteo Gamba, Adrian Chmielewski-Anders, Josephine Sullivan +2
The number of linear regions has been studied as a proxy of complexity for ReLU networks. However, the empirical success of network compression techniques like pruning and knowledg…
An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
Miquel Martí i Rabadán, Sebastian Bujwid, Alessandro Pieropan +2
Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. W…
Consistency Regularization Can Improve Robustness to Label Noise
Erik Englesson, Hossein Azizpour
Consistency regularization is a commonly-used technique for semi-supervised and self-supervised learning. It is an auxiliary objective function that encourages the prediction of th…