DEEp learning

DEVCON 2024

Organized BY

Aug 24

Virtual

4th Edition

Deep Learning DevCon (DLDC) is an influential conference on deep learning. Join us to hear some of the leading professionals & researchers that are pushing the boundaries of this very interesting area.
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bring out the

latest research

and

advancements in

DEEP LEARNING

Since deep learning is such a broad subarea of ​​artificial intelligence & machine learning, the volume of problems derived from this approach is very extensive. DLDC plans to uncover those approaches and bring out the latest research and advancements in this field.

The summit will feature talks, paper presentations, exhibitions & hackathons. There will also be a track on workshops on deep learning.

Visionary Speaker

Worldwide Event

Level-up Your Skills in Deep learning

Find Your Tribe

Call

for

papers

DLDC welcomes submissions reporting research that advances deep learning, broadly conceived. The conference scope includes all subareas of deep learning. We expressly encourage work that cuts across technical areas or develops DL techniques in the context of important application domains, such as healthcare, sustainability, transportation, and commerce.

Submissions closing
World Class

OUR

Speakers

Free

Aug 24

Virtual

4th Edition

Deep Learning DevCon (DLDC) is an influential conference on deep learning. Join us on to hear some of the leading professionals & researchers that are pushing the boundaries of this very interesting area.

The Schedule

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  • DLDC 2024

    Aug 24, 2024

  • This talk delves into the intricacies of understanding generative AI models, highlighting the challenges in deciphering how these models produce their outputs. It underscores the necessity for developing interpretable and transparent AI systems to enhance trust and accountability. Emphasizing the critical need for explainability, the discussion aims to bridge the gap between AI complexity and user comprehension.

  • This talk explores the integration of human feedback loops into generative AI models, emphasizing how such feedback can refine outputs and steer the learning process more effectively. It examines research findings on the benefits of incorporating human insights, aiming to enhance the accuracy and relevance of generative AI systems through targeted guidance.

  • This research addresses a critical business challenge faced by commodity trading companies in buying and selling commodities from international markets. Typically, trades can happen in two ways: fixed bid basis or index-linked basis. We explore the use of Large Language Models (LLMs) for time series forecasting to develop a swap-based hedging strategy that aids in deciding between fixed bid and index-linked purchases. Our approach integrates LLM-driven forecasts with swap instruments to create a hedging strategy that mitigates market volatility risks. Through comprehensive empirical analysis and back-testing, we demonstrate the efficacy of LLMs in generating long-term forecasts, thereby enhancing traditional hedging methods. This study also covers various experiments evaluating five LLMs and the finalized ensemble approach that combines traditional ML models with LLMs, providing a robust framework for effective risk management in commodity trading.

  • In an era rife with misinformation, there's a pressing need to combat its spread effectively. This session explores how generative AI models are revolutionizing the fight against disinformation. Learn how these models create counterfeit content to expose manipulative tactics, offering new strategies to uphold truth in the digital realm.

  • This emerging field presents intellectual challenges in developing new algorithms for scalable personalization and has a profound societal impact by revolutionizing education. Professionals in this field have the opportunity to directly influence how individuals acquire knowledge and skills, pushing the boundaries of AI. The potential to reshape education is a powerful motivator in this field. Beyond technical intrigue, there is a data-driven frontier. Expertise in educational data analysis is crucial for optimizing learning journeys. Developers and data scientists can explore innovative model training and data analysis techniques. The applications extend beyond classrooms to corporate training and personalized learning apps, ensuring a wide range of projects catering to diverse interests. Moreover, the rapidly growing demand for skilled professionals translates to excellent career prospects for those at the forefront of this transformative field. Research indicates that the global education market will reach $10 trillion by 2030. By delving into Generative AI and Large Language Models for Learning and Development, developers and data scientists can tackle stimulating challenges and contribute to a future of accessible, engaging, and impactful learning for all.

  • ince the earliest AI research and applications at the end of the 1970s, there has been a fascination for how AI could be used in educational contexts. Over the years, to the present day this has involved three broad focus areas for computer science: hypermedia systems, recommender systems, and more advanced personalization systems, including chatbots usage. At the same time, in related practice areas, a focus on dynamic data and information based systems, including learning management and student information systems, have been evolving. While in games and interactive digital design, notions around experience-based and activity-led learning pedagogy have combined to deliver serious games, interactive animations and simulations applications particularly in medical and military training contexts. While AIED, as a discipline area has been slowly brewing, a considerable knowledge base has been growing, including, many demonstrators, research papers and tools, which have emerged over this period. Recently, the social, economic and political impact of generative, and fully dynamic, information systems, coupled with advances in processing power, presents an acceleration on this trend. The accumulated digital technologies clearly allow us to bring together these threads of research and different types of applications, but present significant ethical challenges. This presentation will consider the context, technologies, and research findings, indicating clear routes for educators, professionals, managers and policy makers to take to ensure safe and secure digital futures.

  • This research aims to enhance large-scale language models (LLMs) by leveraging human preferences through conditional reinforcement learning. It addresses shortcomings in current LLMs affecting authenticity, security, and user engagement. By integrating human preferences and structured decision explanations, the study aims to mitigate discrepancies between LLM outputs and human responses, advancing AI towards more accurate and contextually aware communication models.

  • Credit assessments are pivotal in Government lending, Banking, and Financial Services, shaping credit availability and terms for individuals and businesses. Traditional methods face limitations like narrow knowledge and segmented tasks. This study explores the potential of Large Language Models (LLMs) in credit assessments, presenting a comprehensive framework. Our benchmark includes 27,000 clustered datasets and 81,000 tuning samples tailored for credit evaluation, with an in-depth analysis of LLM biases. Introducing LMiCA, our novel system integrates LLMs to interpret diverse credit data effectively. Evaluations against state-of-the-art methods and open LLMs highlight their superior performance, promising more equitable and accurate credit assessments, potentially broadening financial access. Keywords—Credit Assessments, Large Language Models

  • Unveil the intricacies of crafting explainable Generative AI solutions utilizing LLMOps methodologies. You will gain insights into operationalizing Large Language Models (LLMs) to foster transparency and interpretability in AI-generated content, ensuring accountability, trustworthiness, and user comprehension in diverse applications.

  • Learn why and how data scientists and business professionals can collaborate effectively to maximize the value of data and AI projects. Discover why cross-functional collaboration is essential for successful data projects, how you can overcome common challenges in aligning technical and business perspectives and bridge the communication gap between data teams and stakeholders. This session will equip you with strategies to enhance cross-functional partnerships and drive data-informed success.


Aug 24

Virtual

4th Edition

Deep Learning DevCon (DLDC) is an influential conference on deep learning. Join us on to hear some of the leading professionals & researchers that are pushing the boundaries of this very interesting area.