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Found 9 papers, 3 papers with code
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Specifically, we extend the random features method of Rahimi & Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data.
Von Mises Mixture Distributions for Molecular Conformation Generation
The resulting distribution on geometries $p(x)$ is known as the Boltzmann distribution, and many molecular properties are expectations computed under this distribution.
Deep imitation learning for molecular inverse problems
We show for a wide variety of molecules we can quickly compute the correct molecular structure, and can detect with reasonable certainty when our method cannot.
Cloud Programming Simplified: A Berkeley View on Serverless Computing
Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud.
Operating Systems
Flare Prediction Using Photospheric and Coronal Image Data
This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view.
Automatic discovery of cell types and microcircuitry from neural connectomics
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure.
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature beta(t) in an annealing schedule.
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude.
