Leland Stanford Junior University

Sandisk Technologies Inc.

Datarobot, Inc.

Bharath Ramsundar

Nisha D Talagala

Swaminathan Sundararaman

Sriram Subramanian

Vijay S Pande

Evan N Feinberg

Sindhu Ghanta

Drew Roselli

Vinay Sridhar

Lior Khermosh

Ashish Batwara

Lior Amar

Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

Detecting suitability of machine learning models for datasets

Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Systems and methods for spatial graph convolutions with applications to drug discovery and molecular simulation

Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Machine learning abstraction

Apparatuses, systems, methods, and computer program products are disclosed for conditional iteration. A method includes receiving a request comprising a condition. A method includes checking an address mapping structure for entries satisfying a condition for a request. A method includes providing a result for a request based on one or more entries satisfying a condition for a request.

Conditional iteration for a non-volatile device

Apparatuses, systems, and methods are disclosed for a key-value store. A method includes encoding a key of a key-value pair into a logical address of a sparse logical address space for a non-volatile medium. A method includes mapping a logical address to a physical location in the non-volatile medium. A method includes storing a value of a key-value pair at a physical location.

Non-volatile key-value store

Growing community of inventors

Fremont, CA, United States of America

Bharath Ramsundar