2 citations · 6 across the 6 of their papers we have counts for
6 papers
Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation
Uri Stern, Eli Corn, Daphna Weinshall
Overfitting in deep neural networks occurs less frequently than expected. This is a puzzling observation, as theory predicts that greater model capacity should eventually lead to o…
On Local Overfitting and Forgetting in Deep Neural Networks
Uri Stern, Tomer Yaacoby, Daphna Weinshall
The infrequent occurrence of overfitting in deep neural networks is perplexing: contrary to theoretical expectations, increasing model size often enhances performance in practice.…
The Paradox of Function Header Comments
Arthur Oxenhorn, Almog Mor, Uri Stern +1
Perhaps the most widely used form of code documentation is function header comments. We performed a large-scale survey of 367 developers to catalog their expectations from such doc…
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs
Uri Stern, Daphna Weinshall
The infrequent occurrence of overfit in deep neural networks is perplexing. On the one hand, theory predicts that as models get larger they should eventually become too specialized…
United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit
Uri Stern, Daniel Shwartz, Daphna Weinshall
Deep neural networks have become the method of choice for solving many classification tasks, largely because they can fit very complex functions defined over raw data. The downside…
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Daniel Shwartz, Uri Stern, Daphna Weinshall
Deep neural networks have incredible capacity and expressibility, and can seemingly memorize any training set. This introduces a problem when training in the presence of noisy labe…