Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

Pruning is an effective way to reduce the huge inference cost of Transformer models.
However, prior work on pruning Transformers requires retraining the models. This can add …

The recent emergence of Large Language Models based on the Transformer architecture
has enabled dramatic advancements in the field of Natural Language Processing. However …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

With the success of language pretraining, it is highly desirable to develop more efficient
architectures of good scalability that can exploit the abundant unlabeled data at a lower cost …

Pre-trained Transformer-based models have achieved state-of-the-art performance for
various Natural Language Processing (NLP) tasks. However, these models often have …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

In this paper, we address the text and image matching in cross-modal retrieval of the fashion
industry. Different from the matching in the general domain, the fashion matching is required …