KDD 2022 Deep Learning Day

KDD 2022 Deep Learning Day

The impact of deep learning on the world has been nothing short of transformative. Powered by the surge in modern compute capacities, widespread data availability, and advances in coding frameworks, deep neural networks are ubiquitous. Deep methods yield state-of-the-art performance in various domains (e.g., computer vision, speech recognition and generation, natural language processing and understanding, recommendation systems), and are still widening their lead as more advanced methods are developed and new applications emerge on a regular basis. Currently, there is interest in both theoretical and practical aspects of deep learning, including interpretable and transparent theory and models that can further help understand the great empirical successes that many real-world applications have been enjoying, as well as data and compute efficiency, reliability, robustness and safety, privacy and ethical considerations, and more.

At KDD 2022, the Deep Learning Day is a key event, specifically dedicated to the impact of deep learning on data science. The goal will be to provide a broad overview of recent developments in deep learning, including emerging topics that deserve more attention. The Deep Learning Day will include exciting plenary keynotes by thought leaders and short presentations by junior researchers from the deep learning and data mining communities, as well as half-day workshops focusing on emerging deep learning topics in KDD.

The KDD Deep Learning Day 2022 will take place on Aug 15, 8am-5pm EST. On behalf of the Deep Learning Day and KDD 2022 organizing committee, we welcome you all to attend this event!

Schedule

  • 8:00–8:10AM Opening Remarks
  • 8:10–8:50AM Invited speaker 1
  • 8:50–9:30AM Invited speaker 2
  • 9:30–10:00AM Short invited presentations by junior researchers
  • 10:00–10:30AM Coffee Break
  • 10:30–11:10AM Invited speaker 3
  • 11:10–12:00PM Panel
  • 12:00–1:00PM Lunch
  • 1:00–5:00PM Deep Learning Day Workshops
    • The 3rd KDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial’22)
    • The 4th Workshop on Adversarial Learning Methods for Machine Learning and Data Mining

    Plenary Keynote Speakers

    TBD

    Organizers

    Chandan Reddy
    Virginia Tech
    Danai Koutra
    Univ. of michigan / Amazon

    Workshops



    The 3rd KDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (DeepSpatial’22)

    Organizers: Zhe Jiang, Zhao Liang, Xun Zhou, Robert Stewart, Junbo Zhang, Shashi Shekhar, Jieping Ye
    Description: The significant advancements in software and hardware technologies stimulated the prosperities of the domains in spatial computing and deep learning algorithms, respectively. Recent breakthroughs in the deep learning field have exhibited outstanding performance in handling data in space and time in specific domains such as image, audio, and video. Meanwhile, the development of sensing and data collection techniques in relevant domains have enabled and accumulated large scale of spatiotemporal data over the years, which in turn has led to unprecedented opportunities and prerequisites for the discovery of macro- and micro- spatiotemporal phenomena accurately and precisely. The complementary strengths and challenges between spatiotemporal data computing and deep learning in recent years suggest urgent needs to bring together the experts in these two domains in prestigious venues, which is still missing until now.

    This workshop will provide a premium platform for both research and industry to exchange ideas on opportunities, challenges, and cutting-edge techniques of deep learning in spatiotemporal data, applications, and systems



    The 4th Workshop on Adversarial Learning Methods for Machine Learning and Data Mining

    Organizers: Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu
    Description: In recent years, adversarial learning methods are shown to be a key technique that leads to exciting breakthroughs and new challenges of many machine learning and data mining tasks. Examples include improved training of generative models (e.g., generative adversarial nets), adversarial robustness of machine learning systems in different domains (e.g., adversarial attacks, defenses, and property verification), and robust representation learning (e.g., adversarial loss for learning embedding), to name a few. Generally speaking, the idea of “learning with an adversary” is crucial for expanding the learning capability, ensuring trustworthy decision making, and enhancing generalizability of machine learning and data mining methods.

    This workshop also aims to bridge theory and practice by encouraging theoretical studies motivated by adversarial ML/DM problems, such as robust (minimax) optimization and game theory.