We are very excited that Martin Mundt is co-chairing and organizing the Queer in AI diversity & inclusion event at the AAAI 2023 conference: https://www.queerinai.com/aaai-2023 Queer in AI’s presence at AAAI 2023 aims to create a safe and inclusive casual networking and socializing space for… Read More »AAAI-23 Queer in AI
Martin Mundt will join Sara Beery, Shamini Kothari and Talia Ringer as a panelist in the NeurIPS 2022 Queer in AI Workshop panel on “Faculty and Queerness” on November 28th. The panel brings together a diverse group of queer faculty to discuss their experiences in… Read More »Martin Mundt will be part of the Queer in AI “Faculty & Queerness” panel at NeurIPS-22
we are extremely excited to be part of the organizer team in the newest effort in advancing the field: the Continual Causality bridge at AAAI-23: https://www.continualcausality.org Continual Causality is a full two-day event (7+8th of February at AAAI-23 in Washington DC, USA): a “bridge” with the aim of bringing together the research… Read More »AAAI-23 Bridge on Continual Causality
We are very pleased to announce that we are co-organizing the workshop on Continual Lifelong Learning at the Asian Conference on Machine Learning (ACML 2022), this December in hybrid format! The physical conference will be held December 8-10th in Hyderabad, India: https://www.acml-conf.org/2022/ The workshop spans many… Read More »Continual Lifelong Learning Workshop at ACML 2022
Our formerly pre-registered paper called “Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable Sampling” is finally out in the Proceedings of Machine Learning Research (PMLR), Volume 181, Pages 1-21. The paper has started as part of the NeurIPS 2021 Preregister Science Workshop, where the… Read More »“Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable Sampling” paper out now in PMLR 181
Our paper titled “Return of the normal distribution: Flexible deep continual learning with variational auto-encoders” is now published in Neural Networks, Volume 154, Pages 397-412, October 2022. In the paper we propose a novel two-stage variational auto-encoder approach to continual learning. Our approach comes with… Read More »“Return of the normal distribution: Flexible deep continual learning with variational auto-encoders” paper now in Neural Networks 154
Group leader Martin Mundt has joined the board of directors of the non-profit ContinualAI organization for the election term of 2022-2024. Following up on the last years’ successes of ContinualAI, the six board members will continue scaling up the organization’s efforts to facilitate public access… Read More »Martin Mundt has joined the board of ContinualAI
We are more than happy to welcome Subarnaduti Paul as one of the first OWLL PhD students at TU Darmstadt & hessian.AI. Subarnaduti Paul has previously successfully graduated from TU Munich in the summer of 2022, before starting the PhD with OWLL in July. His… Read More »Subarnaduti Paul has joined as a PhD student
We are more than happy to welcome Roshni Kamath as one of the first OWLL PhD students at TU Darmstadt & hessian.AI. Roshni has previously worked as a Research Associate/AI Consultant at Forschungszentrum Jülich. She also worked as a Software Engineer before pursuing her Masters… Read More »Roshni Kamath has joined as a PhD student
We are beyond excited to share our newest effort in making continual machine learning accessible to the public: Martin Mundt will be presenting a 13 week long course titled “𝐎𝐩𝐞𝐧 𝐖𝐨𝐫𝐥𝐝 𝐋𝐢𝐟𝐞𝐥𝐨𝐧𝐠 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 – 𝐀 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞”, supported by hessian.AI & the non-profit ContinualAI organization. … Read More »Open World Lifelong Learning – A Continual Machine Learning Course
