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

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

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 …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Large language Models (LLMs), though growing exceedingly powerful, comprises of orders
of magnitude less neurons and synapses than the human brain. However, it requires …

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

Neural Architecture Search (NAS) is a promising and rapidly evolving research area.
Training a large number of neural networks requires an exceptional amount of …