It is well understood from literature that the performance of a machine learning (ML) model is
upper bounded by the quality of the data. While researchers and practitioners have focused …

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the
difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge …

Fine-grained classification involves dealing with datasets with larger number of classes with
subtle differences between them. Guiding the model to focus on differentiating dimensions …

Multi-Task Learning (MTL) networks have emerged as a promising method for transferring
learned knowledge across different tasks. However, MTL must deal with challenges such as …

Data Augmentation approaches often use Language Models, pretrained on large quantities
of unlabeled generic data, to conditionally generate examples. However, the generated data …

This paper seeks to answer the following question:" What can we learn by predicting
accuracy?". Indeed, classification is one of the most popular tasks in machine learning, and …

We present a tool, Text Characterization Toolkit (TCT), that researchers can use to study
characteristics of large datasets. Furthermore, such properties can lead to understanding the …

The international standard to ascertain the cause of death is medical certification. However,
in many low and middle-income countries, the majority of deaths occur outside of health …

In this paper, the structure of a separable convolutional neural network that consists of an
embedding layer, separable convolutional layers, convolutional layer and global average …

In this work, we curate and investigate a dataset named Turkish Software Report-Module
Classification (TSRMC), consisting of commercial software bug reports of a company …