Search Results for author:
Found 6 papers, 3 papers with code
Alternately Optimized Graph Neural Networks
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science.
Towards Unsupervised Segmentation of Extreme Weather Events
Extreme weather is one of the main mechanisms through which climate change will directly impact human society.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Deep learning is a promising tool to determine the physical model that describes our universe.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
