Since its inception in" Attention Is All You Need", transformer architecture has led to
revolutionary advancements in NLP. The attention layer within the transformer admits a …

Recently, a new line of works has emerged to understand and improve self-attention in
Transformers by treating it as a kernel machine. However, existing works apply the methods …

In this paper we revisit techniques for uncertainty estimation within deep neural networks
and consolidate a suite of techniques to enhance their reliability. Our investigation reveals …

The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …

Jigsaw puzzles are a popular and enjoyable pastime that humans can easily solve, even
with many pieces. However, solving a jigsaw is a combinatorial problem, and the space of …

Despite promising advancements in deep learning in medical domains, challenges still
remain owing to data scarcity, compounded by privacy concerns and data ownership …

Archaeological artifacts play important role in understanding the past developments of the
humanity. However, the artifacts are often highly fragmented and degraded, with many …

Most self-supervised learning (SSL) methods often work on curated datasets where the
object-centric assumption holds. This assumption breaks down in uncurated images …

Abstract The emergence of Vision Transformers (ViTs) has marked a significant shift in the
field of computer vision, presenting new methodologies that challenge traditional …

In this paper, we address the problem of noisy datasets by proposing a dual screening
scheme to improve the performance of models trained on two public noisy datasets …