4 accepted papers
Differentiable discrete selection uses soft mixtures during training but hard selection at deployment, resulting in a training-inference gap. We decompose this gap into selection gap (method-dependent, reducible) and computation gap (input-dependent, irreducible). Our key finding: the selection gap …
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper,…
This paper considers classification problems with hierarchically organized classes. We force the classifier (hyperplane) of each class to belong to a sphere manifold, whose center is the classifier of its super-class. Then, individual sphere manifolds are connected based on their hierarchical relati…
While humans can solve a visual puzzle that requires logical reasoning by observing only few samples, it would require training over a large number of samples for state-of-the-art deep reasoning models to obtain similar performance on the same task. In this work, we propose to solve such a few-shot…