DevoWormML
DevoWormML is a collaborative interest group that will explore the application of machine learning and artificial intelligence to problems in developmental biology. Participation is not limited to Machine Learning approaches, and can include various types of intelligent systems approaches. Our goal is to stimulate interest in new techniques, discover new research domains, and establish new collaborations.
Join us for our first meeting on September 6, 2019 at 1pm UTC, and be sure to join our Slack channel!
Google Meet OpenWorm Slack #devowormml (or join Slack #devowormml)
Google Meet OpenWorm Slack #devowormml (or join Slack #devowormml)
September 4: informational first meeting. video, slides
Knowledge Map of Machine Learning, Orthogonal Research and Education Laboratory
September 11: second meeting (Digital Bacillaria). video, slides
September 18: third meeting (OpenDevoCell). video, slides
Gartner Hype Cycle for AI and ML (2019)
Knowledge Map of Machine Learning, Orthogonal Research and Education Laboratory
September 11: second meeting (Digital Bacillaria). video, slides
September 18: third meeting (OpenDevoCell). video, slides
Gartner Hype Cycle for AI and ML (2019)
September 25: fourth meeting (Input Data). video, slides
October 2: fifth meeting (Pre-trained Models for Biology). video, slides
Space of process models, cognitive models vs. machine learning models vs. neural simulations. From: Figure 3 in Kriegeskorte and Douglas, "Cognitive computational neuroscience". arXiv, 1807.11819.
October 2: fifth meeting (Pre-trained Models for Biology). video, slides
Space of process models, cognitive models vs. machine learning models vs. neural simulations. From: Figure 3 in Kriegeskorte and Douglas, "Cognitive computational neuroscience". arXiv, 1807.11819.
October 9: sixth meeting (TensorFlow Tutorial). video, slides
Join us for Hacktoberfest through the month of October. Check out our issues board and make a pull request today!
October 16: seventh meeting (general discussion). video
October 23: eighth meeting (Computational Pareidolia). video, slides
October 30: ninth meeting (Developmental GANs and GANs in Medical Imaging). Video. Developmental GANs slides, GANs in Medical Imaging slides.
NEW! Pre-trained Models for Biology (see October 2 for topic). Blog post at The Node.
November 6: tenth meeting (general discussion). video
November 13: eleventh meeting (Game Theory in Machine Learning and Development). video, slides
November 20: twelfth meeting (Reinforcement Learning). video, slides
November 27: thirteenth meeting (Biological Metaphors and ML, RL, and Cybernetics). video, slides
December 2: fourteenth meeting (Deep Fovea, Medical Imaging, and Peter's Rule). video, Deep Fovea slides, CT Problem slides, Peter's Rule paper
December 9: fifteenth meeting (Invariance and Universal Features). video, slides, Hydrodynamics of Embryo Cell Positioning paper
Artificial neural architectures and a minimization function for the brain? From: Figure 1 in Richards et.al, "A Deep Learning framework for neuroscience". Nature Neuroscience, 22, 1761–1770.
Join us for Hacktoberfest through the month of October. Check out our issues board and make a pull request today!
October 16: seventh meeting (general discussion). video
October 23: eighth meeting (Computational Pareidolia). video, slides
October 30: ninth meeting (Developmental GANs and GANs in Medical Imaging). Video. Developmental GANs slides, GANs in Medical Imaging slides.
NEW! Pre-trained Models for Biology (see October 2 for topic). Blog post at The Node.
November 6: tenth meeting (general discussion). video
November 13: eleventh meeting (Game Theory in Machine Learning and Development). video, slides
November 20: twelfth meeting (Reinforcement Learning). video, slides
November 27: thirteenth meeting (Biological Metaphors and ML, RL, and Cybernetics). video, slides
December 2: fourteenth meeting (Deep Fovea, Medical Imaging, and Peter's Rule). video, Deep Fovea slides, CT Problem slides, Peter's Rule paper
December 9: fifteenth meeting (Invariance and Universal Features). video, slides, Hydrodynamics of Embryo Cell Positioning paper
Artificial neural architectures and a minimization function for the brain? From: Figure 1 in Richards et.al, "A Deep Learning framework for neuroscience". Nature Neuroscience, 22, 1761–1770.
December 16: sixteenth meeting (Hierarchical Temporal Modeling and New Directions). video, slides, Rethinking the Bias-Variance Tradeoff paper, Skepticism of backpropagation in AI discussion
