Link to: Machine Learning
MACHINE LEARNING
Link to: ML People
PEOPLE
Link to: ML Publications
PUBLICATIONS
Link to: ML Patents
PATENTS
Link to: Healthcare Large Multimodal Models
Healthcare Large Multimodal Models
We are focused on developing advanced LMMs that integrate explainable reasoning and safe generation to optimize healthcare workflows. These models are designed to process and analyze multiple data modalities—such as text, images, and structured data—enabling them to assist in complex tasks like diagnostics, treatment planning, and patient management with exceptional accuracy.
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Link to: Human Collaborative LLM Agent
Human Collaborative LLM Agent
This research project is at the forefront of integrating advanced Large Language Models (LLMs) into the process of deriving actionable insights from vast and complex document repositories. This initiative focuses on creating a system that allows users to interact with the LLM, guiding its analysis and refining the results based on the user’s expertise and evolving needs.
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Link to: Trustworthy Generative AI
Trustworthy Generative AI
The Trustworthy Generative AI Project is focused on developing advanced multimodal generative models that can create and reason with content across text, images, reports, and 3D videos. These models are designed for applications in advertisement, entertainment, law enforcement, and healthcare.
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Link to: Digital Pathology
Digital Pathology
In problems with a large number of labels, most multi-label and multi-class techniques incur a significant computational burden at test time. This is because, for each test instance, they need to systematically evaluate every label to decide whether it is relevant for the instance or not.
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Link to: Text Understanding And Factual Generation
Text Understanding And Factual Generation
Our research spans many areas of text understanding and text generation, with a particular emphasis on factuality checking and factually guided text generation. NEC’s products help humans safely draw conclusions from large quantities of text that they don’t have the time to read. As a leader in the FEVER fact extraction and verification competitions, we have developed systems that achieved higher evidence precision and higher robustness to adversarial attack, and pioneered the ability to pursue missing evidence through multiple retrieval steps.
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Link to: Physics Informed Machine Learning
Physics Informed Machine Learning
Since 2019, the parameters of large deep learning models have increased by over 300 times every 18 months. However, the future ML progress cannot continue simply based on using more data or creating larger models, because the growing gap between the model demand and resource supply is not sustainable.
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