The rapid rise of deep generative models signifies a fundamental shift in AI research and applications, prompting significant questions worthy of exploration. There is a resurgence of interest in integrating Generative Adversarial Networks (GANs) with the DM to achieve rapid and high-fidelity generation. This event seeks to spark interactive discussions focused on improving training and sampling efficiency within the domain of DM and GAN.

ML Collective (https://mlcollective.org) is dedicated to the mission of democratizing AI research from a humanistic perspective: providing more opportunities to people in support of their growth in ML research. We have a tradition of hosting socials at major conferences in the past: https://mlcollective.org/events/#social

AI/ML research is progressing at an unprecedented speed; it is more urgent than ever that we break the institutional walls and collaborate.

Founded in 2006, the Women in Machine Learning (WiML) workshop fosters connections, mentorship, and idea exchange among women in machine learning. The social event at ICLR for women and nonbinary individuals, and mentorship sessions, promoting dialogue and collaboration. WiML aims to increase women's representation in tech and foster a diverse, innovative community in the machine learning landscape. Spots are limited - Registration is Required to attend this luncheon - click on Project Page to Register.

Our event aims to gather researchers and practitioners interested in discussing recent advances and promising directions for deep learning interpretability research, with a specific focus on Transformers-based language models and mechanistic approaches aimed at reverse-engineering their behaviors.

We aim to foster meaningful discussions among researchers, practitioners, and policymakers dedicated to creating safer AI technologies. Whether you're deeply entrenched in safety research or keen to learn about the intersection of AI and ethics, your insights and experiences are invaluable to us.

Google Scholar is widely used to form opinions about researchers, but it is not a passive measuring tool. Its deliberate decisions on what to show and what to hide have a massive impact on how science is done today: they influence what researchers decide to work on, their methodologies, and career advancements.

We believe Google Scholar profiles are not serving science in the best way. We wish to share our vision of a Better Scholar for the future and gather your observations and feedback.

A premier networking event between AI researchers, tech entrepreneurs, and investors. Focused on leveraging generative AI for startups, it invites participants to explore ideas, investment opportunities, share insights, and discuss the future of AI in entrepreneurship. Perfect for innovators seeking to blend cutting-edge ML with society impacting applications.

Designing systems to operate safely in real-world settings is a topic of growing interest in machine learning. We want to host a meet-up for researchers who are currently working on or interested in topics relating to AI safety and security, such as adversarial robustness, interpretability, and backdoors, to foster discussion and collaboration. We hosted similar events at NeurIPS and ICML in 2023 which were very well attended (>200 and >150 concurrent attendees, respectively).

The companion social to the DMLR workshop at ICLR. An opportunity to meet others and talk about the future of data and data-centric AI! This is a semi-structured meet up. We'll have roundtables set up with different themes to foster discussion and help you find your people. Possible topics include: data collection, benchmarking techniques, data cleaning, data governance, active learning, data-centric approaches to AI alignment, etc... If time, we will reconvene at end to summarize discussions.

Google Scholar is widely used to form opinions about researchers, but it is not a passive measuring tool. Its deliberate decisions on what to show and what to hide have a massive impact on how science is done today: they influence what researchers decide to work on, their methodologies, and career advancements.

We believe Google Scholar profiles are not serving science in the best way. We wish to share our vision of a Better Scholar for the future and gather your observations and feedback.