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Found 9 papers, 4 papers with code
In Your Pace: Learning the Right Example at the Right Time
Initially, we define the difficulty of a training image using transfer learning from some competitive "teacher" network trained on the Imagenet database, showing improvement in learning speed and final performance for both small and competitive networks, using the CIFAR-10 and the CIFAR-100 datasets.
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance.
Pruning the Unlabeled Data to Improve Semi-Supervised Learning
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 unlabeled data, both drawn from the same underlying distribution.
Active Learning Through a Covering Lens
Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry.
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies.
Ranked #1 on
Active Learning
on CIFAR10 (10,000)
The Grammar-Learning Trajectories of Neural Language Models
These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena.
Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks
Empirically, we show how the PC-bias streamlines the order of learning of both linear and non-linear networks, more prominently at earlier stages of learning.
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
We further show that this pattern of results reflects the interplay between the way neural networks learn benchmark datasets.
On The Power of Curriculum Learning in Training Deep Networks
We address challenge (i) using two methods: transfer learning from some competitive ``teacher" network, and bootstrapping.
