BERT4Rec is an effective model for sequential recommendation based on the Transformer
architecture. In the original publication, BERT4Rec claimed superiority over other available …

As AI models continue to advance into many real-life applications, their ability to maintain
reliable quality over time becomes increasingly important. The principal challenge in this …

Abstract Traditional methods for Unsupervised Domain Adaptation (UDA) targeting semantic
segmentation exploit information common to the source and target domains, using both …

If the same neural network architecture is trained multiple times on the same dataset, will it
make similar linguistic generalizations across runs? To study this question, we fine-tuned …

The quest for determinism in machine learning has disproportionately focused on
characterizing the impact of noise introduced by algorithmic design choices. In this work, we …

Abstract Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE)
models have recently begun to be explored in the context of drug discovery and have the …

Objective. Deep learning (DL) models have been proven to be effective in decoding motor
imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success …

This paper proposes an intelligent text classification framework for a resource-constrained
language like Bengali, which is considered a challenging task due to the lack of standard …

In Natural Language Processing (NLP), the Elo rating system, originally designed for
ranking players in dynamic games such as chess, is increasingly being used to evaluate …

We address the task of automatically detecting and counting seabirds in unmanned aerial
vehicle (UAV) imagery using deep convolutional neural networks (CNNs). Our study area …