In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

Abstract The Neural Tangent Kernel (NTK), defined as the outer product of the neural
network (NN) Jacobians, has emerged as a central object of study in deep learning. In the …

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-
learning from a collection of pre-trained models without access to the training data. Existing …

We propose a new method for approximating active learning acquisition strategies that are
based on retraining with hypothetically-labeled candidate data points. Although this is …

Empirical neural tangent kernels (eNTKs) can provide a good understanding of a given
network's representation: they are often far less expensive to compute and applicable more …

Catastrophic forgetting (CF) frequently occurs when learning with non-stationary data
distribution. The CF issue remains nearly unexplored and is more challenging when meta …

Existing meta-learning works assume that each task has available training and testing data.
However, there are many available pre-trained models without accessing their training data …

In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of
the attacker is to cause the final trained model to predict the attacker's desired target label …

Infinite width limit has shed light on generalization and optimization aspects of deep learning
by establishing connections between neural networks and kernel methods. Despite their …

The paradigm of machine intelligence moves from purely supervised learning to a more
practical scenario when many loosely related unlabeled data are available and labeled data …