Lifelong Learning of Video Diffusion Models From a Single Video Stream
PLAI group member Jason Yoo and colleagues, under the supervision of Dr. Frank Wood and Dr. Geoff Pleiss, have released a new paper on training autoregressive video diffusion models from a continuous video stream that outputs one video frame at a time. The AI community has long sought models and algorithms that learn in a […]
plaicraft.ai launch
We are proud to announce that UBC’s Behavioral Research Ethics Board has issued a certificate of approval under the minimal risk category for us to publicly release plaicraft.ai, a “free Minecraft in the cloud” generative AI research data collection project. Please consider contributing by signing up and playing Minecraft in your browser at www.plaicraft.ai. Our […]
Visual Chain-of-Thought Diffusion Models
Images generated by our baseline, EDM. They mostly look realistic but there are occasionally artifacts – see the blobs on the chin in the first and seventh images. Images generated by our method. We don’t see any of the artifacts that were present in images from the baseline. At this year’s CVPR workshop on Generative […]
Graphically Structured Diffusion Models
Christian Weilbach and Will Harvey, under the supervision of Dr. Frank Wood (PLAI group), have just released a paper on a new deep generative framework to learn structured diffusion models. In contrast to our approach, picture a traditional algorithm design that requires careful mathematical reasoning and a precise implementation in light of numerical and/or combinatorial […]
Continually Learning Deep Associative Memories
Associative memories are models that store and recall patterns. Pattern recall (associative recall) is a process whereby an associative memory, upon receiving a potentially corrupted memory query, retrieves the associated value from memory. Pattern storage is a process whereby an associative memory adjusts its parameters such that the new pattern can be recalled at a […]
Flexible Diffusion Modeling of Long Videos
PLAI group members Will Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach (under the supervision of Dr. Frank Wood) have just released a paper on an astounding new deep generative model for video. Think OpenAI‘s GPT-3 but, instead of generating text given a prompt, their “Flexible Diffusion Model” (FDM) completes videos given a few frames of context. What is more, […]
Research Associate in Machine Learning for Physics
The Departments of computer science at the University of British Columbia, Vancouver campus invite applications for a 12-month full time position at the rank of Research Associate. The position may be extended by mutual agreement and funding availability. Salary will be negotiated with the fellow and will be commensurate with level of experience and subject […]
Postdoctoral Fellowship Opportunity – Machine Learning Methods for Physics
The Departments of computer science at the University of British Columbia, Vancouver campus invite applications for a 12-month full time position at the rank of Postdoctoral Fellow. The position may be extended by mutual agreement and funding availability. Salary will be negotiated with the fellow and will be commensurate with level of experience and subject […]
Post-Doctoral Fellowship Opportunity – Machine Learning for Data-centric Manufacturing
We are looking for a full-time post-doctoral fellow to work with Professor’s Frank Wood and Trevor Campbell on the Machine Learning for Data-centric Manufacturing project, led by the UBC Data Science Institute and the Composite Research Network. The position will be for 12 months, and may be extended by mutual agreement and funding availability. Salary […]