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Found 6 papers, 2 papers with code
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program.
Simultaneous Missing Value Imputation and Structure Learning with Groups
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
CoRGi: Content-Rich Graph Neural Networks with Attention
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges).
Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge
In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers.
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students.
Efficient Differentiable Programming in a Functional Array-Processing Language
We present a system for the automatic differentiation of a higher-order functional array-processing language.
