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Our workshop proposal AI for Earth sciences seeks to bring cutting edge geoscientific and planetary challenges to the fore for the machine learning and deep learning communities. We seek machine learning interest from major areas encompassed by Earth sciences which include, atmospheric physics, hydrologic sciences, cryosphere science, oceanography, geology, planetary sciences, space weather, volcanism, seismology, geo-health (i.e. water, land, air pollution, environmental epidemics), biosphere, and biogeosciences. We also seek interest in AI applied to energy for renewable energy meteorology, thermodynamics and heat transfer problems. We call for papers demonstrating novel machine learning techniques in remote sensing for meteorology and geosciences, generative Earth system modeling, and transfer learning from geophysics and numerical simulations and uncertainty in Earth science learning representations. We also seek theoretical developments in interpretable machine learning in meteorology and geoscientific models, hybrid models with Earth science knowledge guided machine learning, representation learning from graphs and manifolds in spatiotemporal models and dimensionality reduction in Earth sciences. In addition, we seek Earth science applications from vision, robotics, multi-agent systems and reinforcement learning. New labelled benchmark datasets and generative visualizations of the Earth are also of particular interest. A new area of interest is in integrated assessment models and human-centered AI for Earth.
AI4Earth Areas of Interest:
- Atmospheric Science
- Hydro and Cryospheres
- Solid Earth
- Theoretical Advances
- Remote Sensing
- Energy in the Earth system
- Extreme weather & climate
- Geo-health
- Biosphere & Biogeosciences
- Planetary sciences
- Benchmark datasets
- People-Earth
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Sat 6:00 a.m. -
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Link to Gather.Town for Casual Conversation link » | 🔗 |
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Sat 6:45 a.m. - 6:55 a.m.
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Introduction and opening remarks
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Introduction
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AI for Earth Sciences, Workshop Founder & Chair, S. Karthik Mukkavilli |
Surya Karthik Mukkavilli 🔗 |
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Sat 6:55 a.m. - 6:58 a.m.
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Sensors and Sampling
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Session Introduction
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SlidesLive Video » Sensors and Sampling, Session Chair, Johanna Hansen |
Johanna Hansen 🔗 |
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Sat 6:58 a.m. - 7:22 a.m.
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Yogesh Girdhar - Enabling Vision Guided Interactive Exploration in Bandwidth Limited Environments
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Sensing and Sampling - Session Keynote
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link »
SlidesLive Video » WARPLab's research focuses on both the science and systems of exploration robots in extreme, communication starved environments such as the deep sea. It aims to develop robotics and machine learning-based techniques to enable search, discovery, and mapping of natural phenomena that are difficult to observe and study due to various physical and information-theoretic challenges. WARPLab is headed by Yogesh Girdhar, and is part of the Deep Submergence Laboratory (DSL), and the Applied Ocean Physics & Engineering (AOPE) department at Woods Hole Oceanographic Institution. |
Yogesh A Girdhar 🔗 |
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Sat 7:22 a.m. - 7:35 a.m.
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Eyes in the sky without boots on the ground: Using satellites and machine learning to monitor agriculture and food security during COVID-19
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Sensors and Sampling - Invited Talk
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SlidesLive Video » Talk Title: "Eyes in the sky without boots on the ground: Using satellites and machine learning to monitor agriculture and food security during COVID-19" Hannah Kerner is an Assistant Research Professor at the University of Maryland, College Park. Her research focuses on developing machine learning solutions for remote sensing applications in agricultural monitoring, food security, and Earth/planetary science. She is the Machine Learning Lead and U.S. Domestic Co-Lead for NASA Harvest, NASA’s food security initiative run out of the University of Maryland. |
Hannah Kerner 🔗 |
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Sat 7:35 a.m. - 7:58 a.m.
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Autonomous Robot Manipulation for Planetary Science: Mars Sample Return, Climbing Lava Tubes
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Sensing and Sampling - Invited Talk
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link »
SlidesLive Video » Talk Title: Autonomous Robot Manipulation for Planetary Science: Mars Sample Return, Climbing Lava Tubes This talk will highlight work at NASA on robotic missions from a machine vision perspective. The discussion will focus on the science questions that NASA hopes to answer through returned samples from Mars and the challenges imposed on robotic systems used for scientific data collection. Related Papers: http://renaud-detry.net/publications/Pham-2020-AEROCONF.pdf https://www.liebertpub.com/doi/10.1089/ast.2019.2177 Renaud Detry is the group leader for the Perception Systems group at NASA's Jet Propulsion Laboratory (JPL). Detry earned his Master's and Ph.D. degrees in computer engineering and robot learning from ULiege in 2006 and 2010. He served as a postdoc at KTH and ULiege between 2011 and 2015, before joining the Robotics and Mobility Section at JPL in 2016. His research interests are perception and learning for manipulation, robot grasping, and mobility, for terrestrial and planetary applications. At JPL, Detry leads the machine-vision team of the Mars Sample Return surface mission, and he leads and contributes to a variety of research projects related to industrial robot manipulation, orbital image understanding, in-space assembly, and autonomous wheeled or legged mobility for Mars, Europa, and Enceladus. |
Renaud Detry 🔗 |
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Sat 7:58 a.m. - 8:06 a.m.
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DeepFish: A realistic fish‑habitat dataset to evaluate algorithms for underwater visual analysis
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Sensors and Sampling - Invited Talk
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link »
SlidesLive Video » Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. However, current datasets for fish analysis tend to focus on the classification task within constrained, plain environments which do not capture the complexity of underwater fish habitats. To address this limitation, we present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine-environments of tropical Australia. The dataset originally contained only classification labels. Thus, we collected point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Our experiments provide an in-depth analysis of the dataset characteristics, and the performance evaluation of several state-of-the-art approaches based on our benchmark. Although models pre-trained on ImageNet have successfully performed on this benchmark, there is still room for improvement. Therefore, this benchmark serves as a testbed to motivate further development in this challenging domain of underwater computer vision. |
Alzayat Saleh · Issam Hadj Laradji · David Vázquez 🔗 |
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Sat 8:06 a.m. - 8:20 a.m.
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Automatic three‐dimensional mapping for tree diameter measurements in inventory operations
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Sensors and Sampling - Invited Talk
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link »
SlidesLive Video » Forestry is a major industry in many parts of the world, yet this potential domain of application area has been overlooked by the robotics community. For instance, forest inventory, a cornerstone of efficient and sustainable forestry, is still traditionally performed manually by qualified professionals. The lack of automation in this particular task, consisting chiefly of measuring tree attributes, limits its speed, and, therefore, the area that can be economically covered. To this effect, we propose to use recent advancements in three‐dimensional mapping approaches in forests to automatically measure tree diameters from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging and large‐scale forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new publicly‐available dataset which includes four different forest sites, 11 trajectories, totaling 1458 tree observations, and 14,000 m2. From our extensive validation, we concluded that our mapping method is usable in the context of automated forest inventory, with our best diameter estimation method yielding a root mean square error of 3.45 cm for our whole dataset and 2.04 cm in ideal conditions consisting of mature forest with well‐spaced trees. Furthermore, we release this dataset to the public (https://norlab.ulaval.ca/research/montmorencydataset), to spur further research in robotic forest inventories. Finally, stemming from this large‐scale experiment, we provide recommendations for future deployments of mobile robots in a forestry context. Jean-François is a Ph.D. student at McGill’s Mobile Robotics Lab, under the supervision of prof. Dave Meger. He is interested in model-based RL for mobile robot navigation in unstructured environments such as forests, tundra or underwater. Previously he was a masters student at the Northern Robotics Laboratory (Norlab), working on lidar mapping and perception for forestry applications. |
Jean-François Tremblay 🔗 |
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Sat 8:20 a.m. - 8:55 a.m.
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Q/A and Discussion for Sensing & Sampling Session
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Q/A and Discussion
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Moderated by Johanna Hansen |
Johanna Hansen · Yogesh A Girdhar · Hannah Kerner · Renaud Detry 🔗 |
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Sat 8:55 a.m. - 9:00 a.m.
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Ecology
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Session Introduction
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SlidesLive Video » Ecology, Session Chair, Natasha Dudek |
Natasha Dudek 🔗 |
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Sat 9:00 a.m. - 9:25 a.m.
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Dan Morris
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Keynote
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link »
SlidesLive Video » Program Director of Microsoft AI for Earth |
Dan Morris 🔗 |
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Sat 9:25 a.m. - 9:55 a.m.
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Giulio De Leo
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Ecology - Session Keynote
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link »
SlidesLive Video » Talk Title (tentative): ML and control of parasitic diseases of poverty in tropical and subtropical countries, with a special focus on schistosomiasis Professor at Stanford University Senior Fellow at Stanford Woods Institute for the Environment |
Giulio De Leo 🔗 |
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Sat 9:55 a.m. - 10:05 a.m.
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Graph Learning for Inverse Landscape Genetics
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Regular Talk - Ecology Session
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SlidesLive Video » |
Prathamesh Dharangutte 🔗 |
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Sat 10:05 a.m. - 10:15 a.m.
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Segmentation of Soil Degradation Sites in Swiss Alpine Grasslands with Deep Learning
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Regular Talk - Ecology Session
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SlidesLive Video » |
Maxim Samarin 🔗 |
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Sat 10:15 a.m. - 10:20 a.m.
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Novel application of Convolutional Neural Networks for the meta-modeling of large-scale spatial data
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Lightning Talk - Ecology Session
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SlidesLive Video » |
Kiri Stern 🔗 |
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Sat 10:20 a.m. - 10:25 a.m.
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Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
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Lightning Talk - Ecology Session
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SlidesLive Video » |
Miguel Morata Dolz 🔗 |
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Sat 10:25 a.m. - 10:30 a.m.
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Interpreting the Impact of Weather on Crop Yield Using Attention
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Lightning Talk - Ecology Session
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SlidesLive Video » |
Tryambak Gangopadhyay 🔗 |
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Sat 10:30 a.m. - 10:55 a.m.
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Q/A and Discussion for Ecology Session
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Q/A and Discussion
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Moderated by Natasha Dudek |
Natasha Dudek · Dan Morris · Giulio De Leo 🔗 |
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Sat 10:55 a.m. - 11:00 a.m.
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Water
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Session Introduction
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By S. Karthik Mukkavilli |
Surya Karthik Mukkavilli 🔗 |
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Sat 11:00 a.m. - 11:25 a.m.
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Pierre Gentine
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Water - Session Keynote
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link »
SlidesLive Video » |
Pierre Gentine 🔗 |
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Sat 11:25 a.m. - 11:40 a.m.
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A Machine Learner's Guide to Streamflow Prediction
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Spotlight Talk - Water Session
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SlidesLive Video » Long Oral (15m) |
Martin Gauch 🔗 |
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Sat 11:40 a.m. - 11:55 a.m.
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A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling
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Spotlight Talk - Water Session
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SlidesLive Video » Long Talk (15m) |
Grey Nearing 🔗 |
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Sat 11:55 a.m. - 12:10 p.m.
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Dynamic Hydrology Maps from Satellite-LiDAR Fusion
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Spotlight Talk - Water Session
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SlidesLive Video » Long Talk (15m) |
Gonzalo M García 🔗 |
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Sat 12:10 p.m. - 12:20 p.m.
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Efficient Reservoir Management through Deep Reinforcement Learning
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Regular Talk - Water Session
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SlidesLive Video » |
Xinrun Wang 🔗 |
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Sat 12:20 p.m. - 12:45 p.m.
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Q/A and Discussion for Water Session
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Q/A and Discussion
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Moderated by S. Karthik Mukkavilli |
Surya Karthik Mukkavilli · Pierre Gentine · Grey Nearing 🔗 |
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Sat 12:45 p.m. - 1:15 p.m.
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Milind Tambe
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Keynote
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link »
SlidesLive Video » Prof Milind Tambe Director, Center for Research on Computation & Society Gordon McKay Professor of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Mail: Maxwell Dworkin 125, 33 Oxford Street, Cambridge, MA 02138 Director for AI for Social Good Google India Research Center teamcore.seas.harvard.edu/tambe |
Milind Tambe 🔗 |
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Sat 1:15 p.m. - 1:25 p.m.
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Q/A and Discussion
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Surya Karthik Mukkavilli · Mayur Mudigonda · Milind Tambe 🔗 |
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Sat 1:25 p.m. - 1:30 p.m.
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Atmosphere
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Session Introduction
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SlidesLive Video » By Tom Beucler |
Tom Beucler 🔗 |
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Sat 1:30 p.m. - 1:55 p.m.
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Michael Pritchard
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Atmosphere - Session Keynote
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link »
SlidesLive Video » |
Mike Pritchard 🔗 |
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Sat 1:55 p.m. - 2:20 p.m.
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Elizabeth Barnes
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Atmosphere - Session Keynote
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link »
SlidesLive Video » Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks |
Elizabeth A. Barnes 🔗 |
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Sat 2:20 p.m. - 2:35 p.m.
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Spatio-temporal segmentation and tracking of weather patterns with light-weight Neural Networks
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Spotlight Talk - Atmosphere Session
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SlidesLive Video » Long Talk (15m) |
Lukas Kapp-Schwoerer 🔗 |
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Sat 2:35 p.m. - 2:50 p.m.
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Leveraging Lightning with Convolutional Recurrent AutoEncoder and ROCKET for Severe Weather Detection
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Spotlight Talk - Atmosphere Session
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SlidesLive Video » Long Talk (15m) |
Nadia Ahmed 🔗 |
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Sat 2:50 p.m. - 2:55 p.m.
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Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery
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Lightning Talk - Atmosphere Session
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SlidesLive Video » |
Valentina Zantedeschi · Valentina Zantedeschi 🔗 |
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Sat 2:55 p.m. - 3:25 p.m.
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Q/A and Discussion for Atmosphere Session
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Q/A and Discussion
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Tom Beucler · Mike Pritchard · Elizabeth A. Barnes 🔗 |
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Sat 3:25 p.m. - 3:30 p.m.
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Simulations, Physics-guided, and ML Theory
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Session Introduction
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By Karthik Kashinath |
Karthik Kashinath 🔗 |
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Sat 3:30 p.m. - 3:55 p.m.
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Stephan Mandt
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Simulations, Physics-guided, and ML Theory - Session Keynote
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link »
SlidesLive Video » |
Stephan Mandt 🔗 |
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Sat 3:55 p.m. - 4:20 p.m.
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Rose Yu
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Simulations, Physics-guided, and ML Theory - Session Keynote
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link »
SlidesLive Video » |
Rose Yu 🔗 |
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Sat 4:20 p.m. - 4:30 p.m.
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Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
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Regular Talk - ML Theory
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SlidesLive Video » |
Hamed Alemohammad 🔗 |
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Sat 4:30 p.m. - 4:40 p.m.
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Multiresolution Tensor Learning for Efficient and Interpretable Spatiotemporal Analysis
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Regular Talk - ML Theory
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SlidesLive Video » |
Raechel Walker 🔗 |
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Sat 4:40 p.m. - 4:50 p.m.
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Climate-StyleGAN : Modeling Turbulent ClimateDynamics Using Style-GAN
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Regular Talk - ML Theory
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Rishabh Gupta 🔗 |
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Sat 4:50 p.m. - 4:55 p.m.
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Interpretable Deep Generative Spatio-Temporal Point Processes
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Lightning Talk - ML Theory
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SlidesLive Video » |
Shixiang Zhu 🔗 |
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Sat 4:55 p.m. - 5:00 p.m.
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Completing physics-based model by learning hidden dynamics through data assimilation
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Lightning Talk - ML Theory Session
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SlidesLive Video » |
Arthur Filoche 🔗 |
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Sat 5:00 p.m. - 5:20 p.m.
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Q/A and Discussion for ML Theory Session
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Q/A and Discussion
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Moderated by Karthik Kashinath and Mayur Mudigonda |
Karthik Kashinath · Mayur Mudigonda · Stephan Mandt · Rose Yu 🔗 |
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Sat 5:20 p.m. - 5:25 p.m.
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People-Earth
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Session Introduction
)
By Mayur Mudigonda |
Mayur Mudigonda 🔗 |
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Sat 5:25 p.m. - 6:00 p.m.
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Q/A and Panel Discussion for People-Earth with Dan Kammen and Milind Tambe
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Q/A and Panel Discussion
)
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Daniel Kammen · Milind Tambe · Giulio De Leo · Mayur Mudigonda · Surya Karthik Mukkavilli 🔗 |
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Sat 6:00 p.m. - 6:05 p.m.
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Solid Earth
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Session Introduction
)
By Kelly Kochanski |
Kelly Kochanski 🔗 |
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Sat 6:05 p.m. - 6:20 p.m.
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Soft Attention Convolutional Neural Networks for Rare Event Detection in Sequences
(
Spotlight Talk - Solid Earth Session
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SlidesLive Video » |
Mandar Kulkarni 🔗 |
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Sat 6:20 p.m. - 6:30 p.m.
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An End-to-End Earthquake Monitoring Method for Joint Earthquake Detection and Association using Deep Learning
(
Regular Talk - Solid Earth
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SlidesLive Video » |
Weiqiang Zhu 🔗 |
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Sat 6:30 p.m. - 6:40 p.m.
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Single-Station Earthquake Location Using Deep Neural Networks
(
Regular Talk - Solid Earth
)
SlidesLive Video » |
S. Mostafa Mousavi 🔗 |
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Sat 6:40 p.m. - 6:45 p.m.
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Framework for automatic globally optimal well log correlation
(
Lightning Talk - Solid Earth Session
)
SlidesLive Video » |
Oleh Datskiv 🔗 |
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Sat 6:45 p.m. - 7:00 p.m.
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Q/A and Discussion for Solid Earth
(
Q/A and Discussion
)
|
Kelly Kochanski 🔗 |
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Sat 7:00 p.m. - 7:05 p.m.
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Benchmark Datasets
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Session Introduction
)
By Karthik Kashinath |
Karthik Kashinath 🔗 |
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Sat 7:05 p.m. - 7:30 p.m.
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Stephan Rasp
(
Benchmark Datasets - Session Keynote
)
link »
SlidesLive Video » |
Stephan Rasp 🔗 |
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Sat 7:30 p.m. - 7:45 p.m.
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RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale
(
Spotlight Talk - Benchmark Datasets
)
SlidesLive Video » Long Talk (15m) |
Catherine Tong 🔗 |
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Sat 7:45 p.m. - 8:00 p.m.
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WildfireDB: A Spatio-Temporal Dataset Combining Wildfire Occurrence with Relevant Covariates
(
Spotlight Talk - Benchmark Datasets Session
)
SlidesLive Video » Long Talk (15m) |
Samriddhi Singla · Tina Diao 🔗 |
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Sat 8:00 p.m. - 8:10 p.m.
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LandCoverNet: A global benchmark land cover classification training dataset
(
Regular Talk - Benchmark Datasets Session
)
SlidesLive Video » |
Hamed Alemohammad 🔗 |
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Sat 8:10 p.m. - 8:20 p.m.
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Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
(
Regular Talk - Benchmark Datasets Session
)
SlidesLive Video » |
Omkar Ranadive 🔗 |
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Sat 8:20 p.m. - 8:25 p.m.
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An Active Learning Pipeline to Detect Hurricane Washover in Post-Storm Aerial Images
(
Lightning Talk - Benchmark Datasets Session
)
SlidesLive Video » |
Evan Goldstein 🔗 |
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Sat 8:25 p.m. - 8:30 p.m.
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Developing High Quality Training Samples for Deep Learning Based Local Climate Classification in Korea
(
Lightning Talk - Benchmark Datasets Session
)
SlidesLive Video » |
Minho Kim 🔗 |
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Sat 8:30 p.m. - 8:55 p.m.
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Q/A and Discussion for Benchmark Datasets
(
Q/A and Discussion
)
|
Karthik Kashinath 🔗 |
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Sat 8:55 p.m. - 9:00 p.m.
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Workshop Closing Remarks
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Surya Karthik Mukkavilli 🔗 |
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Sat 8:55 p.m. - 8:55 p.m.
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Posters
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Posters - Break (On Demand Pre-recorded Not Livestreamed)
)
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Surya Karthik Mukkavilli 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Bias correction of global climate model using machine learning algorithms to determine meteorological variables in different tropical climates of Indonesia
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Poster - Atmosphere Session
)
SlidesLive Video » |
Juan Nathaniel 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Optimising Placement of Pollution Sensors in Windy Environments
(
Poster - Atmosphere Session
)
SlidesLive Video » |
Sigrid Passano Hellan 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Temporally Weighting Machine Learning Models for High-Impact Severe Hail Prediction
(
Poster - Atmosphere Session
)
SlidesLive Video » |
Amanda Burke 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Integrating data assimilation with structurally equivariant spatial transformers: Physically consistent data-driven models for weather forecasting
(
Poster - Atmosphere Session
)
SlidesLive Video » |
Ashesh Chattopadhyay 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale
(
Poster - Atmosphere Session
)
SlidesLive Video » |
Zhonghua Zheng 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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MonarchNet: Differentiating Monarch Butterflies from Those with Similar Appearances
(
Poster - Benchmark Datasets Session
)
SlidesLive Video » |
Thomas Chen 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Nowcasting Solar Irradiance Over Oahu
(
Poster - Atmosphere Session
)
SlidesLive Video » |
Peter Sadowski 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks
(
Poster - ML Theory
)
SlidesLive Video » |
Hamed Alemohammad 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss
(
Poster - Sensing and Sampling Session
)
SlidesLive Video » |
Savas Ozkan 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery
(
Poster - Sensing and Sampling Session
)
SlidesLive Video » |
Thomas Chen 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
(
Poster - Sensing and Sampling Session
)
SlidesLive Video » |
Francesco Pinto 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
|
Domain Adaptive Shake-shake Residual Network for Corn Disease Recognition
(
Poster - Sensing and Sampling Session
)
SlidesLive Video » |
Yuan Fang 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
|
A Comparison of Data-Driven Models for Predicting Stream Water Temperature
(
Poster - Water Session
)
SlidesLive Video » |
Helen Weierbach 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
(
Poster - Water Session
)
SlidesLive Video » |
Nicholas Majeske 🔗 |
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Sat 9:00 p.m. - 9:00 p.m.
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Predicting Streamflow By Using BiLSTM with Attention from heterogeneous spatiotemporal remote sensing products
(
Poster - Water Session
)
SlidesLive Video » |
Udit Bhatia - IITGN 🔗 |
Author Information
Surya Karthik Mukkavilli (University of California, Irvine, Berkeley Lab & McGill)
Johanna Hansen (McGill University)
Natasha Dudek (McGill-Mila)
Tom Beucler (University of California, Irvine)
Kelly Kochanski (University of Colorado Boulder)
Earth science researcher using machine learning to make better predictions about natural hazards and climate change.
Mayur Mudigonda (UC Berkeley)
Karthik Kashinath (LBNL)
Amy McGovern (University of Oklahoma)
Paul D Miller (DJ Spooky)
Chad Frischmann (Drawdown)
Pierre Gentine (Columbia University)
Gregory Dudek (McGill University & Samsung Research)
Aaron Courville (U. Montreal)
Daniel Kammen (University of California, Berkeley)
Vipin Kumar (University of Minnesota)
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Jaideep Pathak · Shashank Subramanian · Peter Harrington · Thorsten Kurth · Andre Graubner · Morteza Mardani · David Hall · Karthik Kashinath · Anima Anandkumar -
2022 : Bayesian Q-learning With Imperfect Expert Demonstrations »
Fengdi Che · Xiru Zhu · Doina Precup · David Meger · Gregory Dudek -
2022 : Imitation from Observation With Bootstrapped Contrastive Learning »
Medric Sonwa · Johanna Hansen · Eugene Belilovsky -
2022 : Datasets That Are Not: Evolving Novelty Through Sparsity and Iterated Learning »
Yusong Wu · Kyle Kastner · Tim Cooijmans · Cheng-Zhi Anna Huang · Aaron Courville -
2022 : Imitation from Observation With Bootstrapped Contrastive Learning »
Medric Sonwa · Johanna Hansen · Eugene Belilovsky -
2022 : Unleashing The Potential of Data Sharing in Ensemble Deep Reinforcement Learning »
Zhixuan Lin · Pierluca D'Oro · Evgenii Nikishin · Aaron Courville -
2022 : Bayesian Q-learning With Imperfect Expert Demonstrations »
Fengdi Che · Xiru Zhu · Doina Precup · David Meger · Gregory Dudek -
2022 : Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier »
Pierluca D'Oro · Max Schwarzer · Evgenii Nikishin · Pierre-Luc Bacon · Marc Bellemare · Aaron Courville -
2022 : Investigating Multi-task Pretraining and Generalization in Reinforcement Learning »
Adrien Ali Taiga · Rishabh Agarwal · Jesse Farebrother · Aaron Courville · Marc Bellemare -
2022 : Imitation from Observation With Bootstrapped Contrastive Learning »
Medric Sonwa · Johanna Hansen · Eugene Belilovsky -
2022 Poster: Riemannian Diffusion Models »
Chin-Wei Huang · Milad Aghajohari · Joey Bose · Prakash Panangaden · Aaron Courville -
2022 Poster: Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 : Amy McGovern: Developing Trustworthy AI for Weather and Climate »
Amy McGovern -
2021 : Behavior Predictive Representations for Generalization in Reinforcement Learning »
Siddhant Agarwal · Aaron Courville · Rishabh Agarwal -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 Poster: Gradient Starvation: A Learning Proclivity in Neural Networks »
Mohammad Pezeshki · Oumar Kaba · Yoshua Bengio · Aaron Courville · Doina Precup · Guillaume Lajoie -
2021 Poster: Pretraining Representations for Data-Efficient Reinforcement Learning »
Max Schwarzer · Nitarshan Rajkumar · Michael Noukhovitch · Ankesh Anand · Laurent Charlin · R Devon Hjelm · Philip Bachman · Aaron Courville -
2021 Poster: A Variational Perspective on Diffusion-Based Generative Models and Score Matching »
Chin-Wei Huang · Jae Hyun Lim · Aaron Courville -
2021 Oral: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 Poster: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2020 : Workshop Closing Remarks »
Surya Karthik Mukkavilli -
2020 : Posters »
Surya Karthik Mukkavilli -
2020 : Q/A and Discussion for Benchmark Datasets »
Karthik Kashinath -
2020 : Benchmark Datasets »
Karthik Kashinath -
2020 : Q/A and Discussion for Solid Earth »
Kelly Kochanski -
2020 : Solid Earth »
Kelly Kochanski -
2020 : Q/A and Panel Discussion for People-Earth with Dan Kammen and Milind Tambe »
Daniel Kammen · Milind Tambe · Giulio De Leo · Mayur Mudigonda · Surya Karthik Mukkavilli -
2020 : People-Earth »
Mayur Mudigonda -
2020 : Q/A and Discussion for ML Theory Session »
Karthik Kashinath · Mayur Mudigonda · Stephan Mandt · Rose Yu -
2020 : Simulations, Physics-guided, and ML Theory »
Karthik Kashinath -
2020 : Q/A and Discussion for Atmosphere Session »
Tom Beucler · Mike Pritchard · Elizabeth A. Barnes -
2020 : Atmosphere »
Tom Beucler -
2020 : Q/A and Discussion »
Surya Karthik Mukkavilli · Mayur Mudigonda · Milind Tambe -
2020 : Q/A and Discussion for Water Session »
Surya Karthik Mukkavilli · Pierre Gentine · Grey Nearing -
2020 : Pierre Gentine »
Pierre Gentine -
2020 : Water »
Surya Karthik Mukkavilli -
2020 : Q/A and Discussion for Ecology Session »
Natasha Dudek · Dan Morris · Giulio De Leo -
2020 : Ecology »
Natasha Dudek -
2020 : Q/A and Discussion for Sensing & Sampling Session »
Johanna Hansen · Yogesh A Girdhar · Hannah Kerner · Renaud Detry -
2020 : Sensors and Sampling »
Johanna Hansen -
2020 : Introduction and opening remarks »
Surya Karthik Mukkavilli -
2020 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais -
2020 Workshop: Differentiable computer vision, graphics, and physics in machine learning »
Krishna Murthy Jatavallabhula · Kelsey Allen · Victoria Dean · Johanna Hansen · Shuran Song · Florian Shkurti · Liam Paull · Derek Nowrouzezahrai · Josh Tenenbaum -
2020 : Opening remarks »
Krishna Murthy Jatavallabhula · Kelsey Allen · Johanna Hansen · Victoria Dean -
2020 Poster: Unsupervised Learning of Dense Visual Representations »
Pedro O. Pinheiro · Amjad Almahairi · Ryan Benmalek · Florian Golemo · Aaron Courville -
2019 : Lunch + Poster Session »
Frederik Gerzer · Bill Yang Cai · Pieter-Jan Hoedt · Kelly Kochanski · Soo Kyung Kim · Yunsung Lee · Sunghyun Park · Sharon Zhou · Martin Gauch · Jonathan Wilson · Joyjit Chatterjee · Shamindra Shrotriya · Dimitri Papadimitriou · Christian Schön · Valentina Zantedeschi · Gabriella Baasch · Willem Waegeman · Gautier Cosne · Dara Farrell · Brendan Lucier · Letif Mones · Caleb Robinson · Tafara Chitsiga · Victor Kristof · Hari Prasanna Das · Yimeng Min · Alexandra Puchko · Alexandra Luccioni · Kyle Story · Jason Hickey · Yue Hu · Björn Lütjens · Zhecheng Wang · Renzhi Jing · Genevieve Flaspohler · Jingfan Wang · Saumya Sinha · Qinghu Tang · Armi Tiihonen · Ruben Glatt · Muge Komurcu · Jan Drgona · Juan Gomez-Romero · Ashish Kapoor · Dylan J Fitzpatrick · Alireza Rezvanifar · Adrian Albert · Olya (Olga) Irzak · Kara Lamb · Ankur Mahesh · Kiwan Maeng · Frederik Kratzert · Sorelle Friedler · Niccolo Dalmasso · Alex Robson · Lindiwe Malobola · Lucas Maystre · Yu-wen Lin · Surya Karthik Mukkavili · Brian Hutchinson · Alexandre Lacoste · Yanbing Wang · Zhengcheng Wang · Yinda Zhang · Victoria Preston · Jacob Pettit · Draguna Vrabie · Miguel Molina-Solana · Tonio Buonassisi · Andrew Annex · Tunai P Marques · Catalin Voss · Johannes Rausch · Max Evans -
2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Poster: Ordered Memory »
Yikang Shen · Shawn Tan · Arian Hosseini · Zhouhan Lin · Alessandro Sordoni · Aaron Courville -
2019 Poster: MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis »
Kundan Kumar · Rithesh Kumar · Thibault de Boissiere · Lucas Gestin · Wei Zhen Teoh · Jose Sotelo · Alexandre de Brébisson · Yoshua Bengio · Aaron Courville -
2019 Poster: No-Press Diplomacy: Modeling Multi-Agent Gameplay »
Philip Paquette · Yuchen Lu · SETON STEVEN BOCCO · Max Smith · Satya O.-G. · Jonathan K. Kummerfeld · Joelle Pineau · Satinder Singh · Aaron Courville -
2018 : Coffee break + posters 1 »
Samuel Myer · Wei-Ning Hsu · Jialu Li · Monica Dinculescu · Lea Schönherr · Ehsan Hosseini-Asl · Skyler Seto · Oiwi Parker Jones · Imran Sheikh · Thomas Manzini · Yonatan Belinkov · Nadir Durrani · Alexander Amini · Johanna Hansen · Gabi Shalev · Jamin Shin · Paul Smolensky · Lisa Fan · Zining Zhu · Hamid Eghbal-zadeh · Benjamin Baer · Abelino Jimenez · Joao Felipe Santos · Jan Kremer · Erik McDermott · Andreas Krug · Tzeviya S Fuchs · Shuai Tang · Brandon Carter · David Gifford · Albert Zeyer · André Merboldt · Krishna Pillutla · Katherine Lee · Titouan Parcollet · Orhan Firat · Gautam Bhattacharya · JAHANGIR ALAM · Mirco Ravanelli -
2018 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Erik Wijmans · Samyak Datta · Ethan Perez · Mateusz Malinowski · Stefan Lee · Peter Anderson · Aaron Courville · Jeremie MARY · Dhruv Batra · Devi Parikh · Olivier Pietquin · Chiori HORI · Tim Marks · Anoop Cherian -
2018 Poster: Improving Explorability in Variational Inference with Annealed Variational Objectives »
Chin-Wei Huang · Shawn Tan · Alexandre Lacoste · Aaron Courville -
2018 Poster: Towards Text Generation with Adversarially Learned Neural Outlines »
Sandeep Subramanian · Sai Rajeswar Mudumba · Alessandro Sordoni · Adam Trischler · Aaron Courville · Chris Pal -
2017 : Poster session 2 and coffee break »
Sean McGregor · Tobias Hagge · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 : Poster session 1 and coffee break »
Tobias Hagge · Sean McGregor · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Abhishek Das · Satwik Kottur · Stefan Lee · Mateusz Malinowski · Olivier Pietquin · Devi Parikh · Dhruv Batra · Aaron Courville · Jeremie Mary -
2017 Poster: Improved Training of Wasserstein GANs »
Ishaan Gulrajani · Faruk Ahmed · Martin Arjovsky · Vincent Dumoulin · Aaron Courville -
2017 Poster: GibbsNet: Iterative Adversarial Inference for Deep Graphical Models »
Alex Lamb · R Devon Hjelm · Yaroslav Ganin · Joseph Paul Cohen · Aaron Courville · Yoshua Bengio -
2017 Poster: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Spotlight: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Adversarially Learned Inference (ALI) and BiGANs »
Aaron Courville -
2016 Poster: Professor Forcing: A New Algorithm for Training Recurrent Networks »
Alex M Lamb · Anirudh Goyal · Ying Zhang · Saizheng Zhang · Aaron Courville · Yoshua Bengio -
2015 : Introduction »
Aaron Courville -
2015 Workshop: Multimodal Machine Learning »
Louis-Philippe Morency · Tadas Baltrusaitis · Aaron Courville · Kyunghyun Cho -
2015 Poster: A Recurrent Latent Variable Model for Sequential Data »
Junyoung Chung · Kyle Kastner · Laurent Dinh · Kratarth Goel · Aaron Courville · Yoshua Bengio -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2013 Demonstration: Topic Modeling for Robots »
Yogesh A Girdhar · Gregory Dudek -
2013 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Session: Oral Session 3: Deep Learning and Network Models »
Aaron Courville -
2008 Session: Oral session 11: Attention and Mind »
Aaron Courville -
2007 Spotlight: The rat as particle filter »
Nathaniel D Daw · Aaron Courville -
2007 Poster: The rat as particle filter »
Nathaniel D Daw · Aaron Courville