The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Large language models (LLMs) have shown excellent performance on various tasks, but the
astronomical model size raises the hardware barrier for serving (memory size) and slows …

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for
Indonesian languages, including opening access to previously non-public resources …

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …