100 citations · 266 across the 9 of their papers we have counts for
11 papers
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds
Owen Melia, Eric Jonas, Rebecca Willett
Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant ma…
Von Mises Mixture Distributions for Molecular Conformation Generation
Kirk Swanson, Jake Williams, Eric Jonas
Molecules are frequently represented as graphs, but the underlying 3D molecular geometry (the locations of the atoms) ultimately determines most molecular properties. However, most…
Cloud Programming Simplified: A Berkeley View on Serverless Computing
Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti +11
Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that great…
Ambitious Data Science Can Be Painless
Hatef Monajemi, Riccardo Murri, Eric Jonas +3
Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Tra…
numpywren: serverless linear algebra
Vaishaal Shankar, Karl Krauth, Qifan Pu +5
Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algeb…
Flare Prediction Using Photospheric and Coronal Image Data
Eric Jonas, Monica G. Bobra, Vaishaal Shankar +2
The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar image data of various wavel…
Occupy the Cloud: Distributed Computing for the 99%
Eric Jonas, Qifan Pu, Shivaram Venkataraman +2
Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation fram…
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
Vikash Mansinghka, Patrick Shafto, Eric Jonas +3
There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a…
Automatic discovery of cell types and microcircuitry from neural connectomics
Eric Jonas, Konrad Kording
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed…
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Fritz Obermeyer, Jonathan Glidden, Eric Jonas
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structur…
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
Vikash Mansinghka, Eric Jonas
The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian in…