Centific’s Post

Qruise is a proud sponsor of the 6th Annual Conference on Learning for Dynamics & Control 💫   Taking place from 15th-17th July at the University of Oxford, the conference will focus on the integration of machine learning, control theory, and optimisation to handle the explosion of real-time data from devices sensing and controlling the physical world ⚡️🌍   Our CEO, Shai Machnes, and Chief Product Officer, Anurag Saha Roy, are looking forward to engaging with the community and expanding their knowledge, so if you have any questions about Qruise or what we do, please don’t hesitate to say hello 👋   #L4DC2024 #MachineLearning #DeepTech

Excited to share the news of our research paper, "Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques" securing acceptance at the distinguished 18th International Conference on Emerging Technologies (ICET)! The conference, hosted in the city of Peshawar, Pakistan, this November, provided an exhilarating stage for exchanging cutting-edge insights in the dynamic realm of technology and innovation. This exceptional milestone is a reflection of the outstanding synergy among my co-authors: Rafay Mustafa, Usama Arshad, Hashim Ali, Zain Ul Abideen, and Abdullah Habib. Your resolute commitment and collaborative ethos played a pivotal role in bringing this project to fruition. Our research makes a substantial contribution to the ongoing dialogue on machine learning techniques in advancing facial emotion recognition. It illuminates both the potential and challenges, paving the way for promising future developments in the domain of facial recognition and its application in computer vision. 🔗 For those eager to delve into the intricacies of our findings, the link to the published paper can be found here: https://lnkd.in/djE5XirC 🙏 Profound appreciation for the unwavering support and guidance from our collaborators and mentors on this incredible journey. Enthusiastic about the potential impact our findings will have on the ever-evolving landscape of computer vision! #ICET2023 #MachineLearning #FacialEmotionRecognition #TechnologyInnovation #ResearchJourney #ComputerVisionImpact

Exciting news to share! Two papers from our team have been accepted in #wacv2024. One paper proposes a new domain agnostic prompt learning technique for CLIP, while the other proposes a novel problem setting of cross-domain and cross-modal retrieval of shape from images. Both works have been thoroughly evaluated and we couldn't be prouder of our co-authors and collaborators. #deeplearning #computervision

I am set to present a research demonstration at MDENet on Wednesday, January 31, from 9:00am. In the session, we focus on our latest project, MAPLe (Model Assistance Prompt Learning). The demo, titled "Assisting Software Model Design: Leveraging Few-Shot Prompt Learning in Model-Driven Engineering," will showcase how MAPLe integrates advanced language models into software model design, aiming to simplify and enhance the process. This tool is developed to tackle the prevalent challenges in the field, leveraging AI for a more effective and adaptable approach in software modeling. Please note that to attend this event, joining the MDENet community is necessary. It's a great opportunity to connect with experts in the field and stay updated on the latest in Model-Driven Engineering. #MDENet #SoftwareModeling #ModelDrivenEngineering #AI4MDE

🌟 New from #NVIDIAResearch: Weight-Decomposed Low-Rank Adaptation (DoRA). A groundbreaking advancement in fine-tuning technology that's set to revolutionize how we optimize pretrained models without increasing inference costs. 👀

🌟 New from #NVIDIAResearch: Weight-Decomposed Low-Rank Adaptation (DoRA). A groundbreaking advancement in fine-tuning technology that's set to revolutionize how we optimize pretrained models without increasing inference costs. 👀

🌟 New from #NVIDIAResearch: Weight-Decomposed Low-Rank Adaptation (DoRA). A groundbreaking advancement in fine-tuning technology that's set to revolutionize how we optimize pretrained models without increasing inference costs. 👀