Results 51 to 60 of about 16,504,576 (273)

On Speculative Decoding for Multimodal Large Language Models

CoRR
Inference with Multimodal Large Language Models (MLLMs) is slow due to their large-language-model backbone which suffers from memory bandwidth bottleneck and generates tokens auto-regressively. In this paper, we explore the application of speculative decoding to enhance the inference efficiency of MLLMs, specifically the LLaVA 7B model.
Mukul Gagrani, Raghavv Goel, Wonseok Jeon, Junyoung Park, Mingu Lee, Christopher Lott   +5 more
openaire   +2 more sources

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

Doctor Sun: a bilingual multimodal large language model for biomedical AI [PDF]

PeerJ Computer Science
While biomedical artificial intelligence (AI) shows great clinical potential, existing systems relying on general-domain large language models (LLMs) often lack sufficient medical data to grasp complex healthcare concepts.
Dong Xue, Ziyao Shao, Zhaoyang Duan, Fangzhou Liu, Bing Li, Zhongheng Zhang   +5 more
doaj   +2 more sources

Semantic Alignment for Multimodal Large Language Models

Proceedings of the 32nd ACM International Conference on Multimedia
Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly in scenarios involving closely resembling images (e.g., change captioning).
Tao Wu, Mengze Li 0001, Jingyuan Chen, Wei Ji 0008, Wang Lin, Jinyang Gao, Kun Kuang 0001, Zhou Zhao 0001, Fei Wu 0001   +8 more
openaire   +3 more sources

Generative Artificial Intelligence in Clinical Practice: How Should Rheumatologists Evaluate and Monitor Emerging Tools?

Arthritis Care &Research, EarlyView.
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo, Mrinalini Dey, Jinoos Yazdany   +2 more
wiley   +1 more source

On opportunities and challenges of large multimodal foundation models in education

npj Science of Learning
Recently, the option to use large language models as a middleware connecting various AI tools and other large language models led to the development of so-called large multimodal foundation models, which have the power to process spoken text, music ...
Stefan Küchemann, Karina E. Avila, Yavuz Dinc, Chiara Hortmann, Natalia Revenga, Verena Ruf, Niklas Stausberg, Steffen Steinert, Frank Fischer, Martin Fischer, Enkelejda Kasneci, Gjergji Kasneci, Thomas Kuhr, Gitta Kutyniok, Sarah Malone, Michael Sailer, Albrecht Schmidt, Matthias Stadler, Jochen Weller, Jochen Kuhn   +19 more
doaj   +1 more source

QueryMintAI: Multipurpose Multimodal Large Language Models for Personal Data

IEEE Access
QueryMintAI, a versatile multimodal Language Learning Model (LLM) designed to address the complex challenges associated with processing various types of user inputs and generating corresponding outputs across different modalities.
Ananya Ghosh, K. Deepa
doaj   +1 more source

Artificial Intelligence‐Driven Extraction of Patient‐Reported Pain Outcomes in Osteoarthritis Using Large Language Models

Arthritis Care &Research, Accepted Article.
Objective Patient‐reported outcome measures (PROMs) are central to assessing symptom burden and treatment response in Osteoarthritis (OA) but are often embedded in unstructured clinical notes. We evaluated whether prompt‐engineered large language models (LLMs) could accurately extract or infer pain scores on a 0‐10 scale from routine OA clinical ...
Jainesh Doshi, Stephen Batter, Yiyuan Wu, William Salerno, Alice Santilli, Sandhya Kannayiram, Susan Goodman, Bella Mehta   +7 more
wiley   +1 more source

Emotion Recognition from Videos Using Multimodal Large Language Models

Future Internet
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and
Lorenzo Vaiani, Luca Cagliero, Paolo Garza   +2 more
doaj   +1 more source

Visual cognition in multimodal large language models

Nature Machine Intelligence
Abstract A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely
Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz   +3 more
openaire   +4 more sources