150 citations · 488 across the 33 of their papers we have counts for
5 papers · 1 filter
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…
Pruning the Unlabeled Data to Improve Semi-Supervised Learning
Guy Hacohen, Daphna Weinshall
In the domain of semi-supervised learning (SSL), the conventional approach involves training a learner with a limited amount of labeled data alongside a substantial volume of unlab…
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
Noam Fluss, Guy Hacohen, Daphna Weinshall
Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that…
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
Guy Hacohen, Daphna Weinshall
In the domain of Active Learning (AL), a learner actively selects which unlabeled examples to seek labels from an oracle, while operating within predefined budget constraints. Impo…