It is important to understand the mathematical foundations of neural networks and to include
robustness in model evaluation. Here, we introduce algorithms based on manifold curvature …

In educational settings of Pakistan, where English is utilized as the primary medium of
instruction but not as an official language, the assessment of instructional text readability is …

In this work, we parallelize high-level features in deep networks to selectively skip or select
class-specific features to reduce inference costs. This challenges most deep learning …

This paper introduces a novel method for creating a metric to measure curvature of a
discretized manifold. For each data point xi on a manifold, a subspace S_i is matched using …

The existence of salient semantic clusters in the latent spaces of a neural network during
training strongly correlates its final accuracy on classification tasks. This paper proposes a …

This paper introduces a novel method that can be used to determine relative robustness of
multiple networks using only training data set without any need for regular or adversarial test …

This paper proposes a novel metric for evaluating the quality of images of the same category
generated by a synthetic image generator. The proposed method assumes that the points …

This paper introduces a novel network topology that seamlessly integrates dynamic
inference cost with a topdown attention mechanism, addressing two significant gaps in …