Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

Large language models have shown their remarkable capabilities as a general interface for
various language-related applications. Motivated by this, we target to build a unified …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

The growing interest in vision-language models (VLMs) has been driven by improvements in
large language models and vision transformers. Despite the abundance of literature on this …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

Large multimodal models trained on natural documents, which interleave images and text,
outperform models trained on image-text pairs on various multimodal benchmarks …

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