Poster Session
in
Workshop: Tackling Climate Change with Machine Learning

Poster Session 2

[ Abstract ]

Abstract:

Poster sessions take place in the following Topia space: https://topia.io/neurips-2022-workshop-tccml

The links below provide access to the video presentations, Rocket.chat, direct Topia links and further materials featured on the workshop website.

Papers Track:

  1. Function Approximations for Reinforcement Learning Controller for Wave Energy Converters
  2. Bayesian inference for aerosol vertical profiles
  3. Machine learning emulation of a local-scale UK climate model
  4. Learning evapotranspiration dataset corrections from water cycle closure supervision
  5. Optimizing Japanese dam reservoir inflow forecast for efficient operation
  6. Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid
  7. Towards a spatially transferable super resolution model for downscaling Antarctic surface melt
  8. Exploring Randomly Wired Neural Networks for Climate Model Emulation
  9. SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications
  10. AutoML for Climate Change: A Call to Action
  11. Reconstruction of Grid Measurements in the Presence of Adversarial Attacks
  12. Climate Policy Radar: Pipeline for automated analysis of public climate policies
  13. Controllable Generation for Climate Modeling
  14. Positional Encoder Graph Neural Networks for Geographic Data
  15. Image-based Early Detection System for Wildfires
  16. Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) clouds
  17. Transformers for Fast Emulation of Atmospheric Chemistry Box Models
  18. Neural Representation of the Stratospheric Ozone Chemistry
  19. DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
  20. An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
  21. An Interpretable Model of Climate Change Using Correlative Learning
  22. Multimodal Wildland Fire Smoke Detection
  23. Accessible Large-Scale Plant Pathology Recognition
  24. Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer
  25. Calibration of Large Neural Weather Models
  26. Learning Surrogates for Diverse Emission Models

Proposals Track:

  1. Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery
  2. Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions
  3. Deep learning-based bias adjustment of decadal climate predictions
  4. Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery
  5. Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods
  6. Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times
  7. Personalizing Sustainable Agriculture with Causal Machine Learning

Tutorials Track:

  1. Automating the creation of LULC datasets

Chat is not available.