RECOMMENDATIONS

R0:3d92323b5375746d21dcb172e8950adc-Explainability in Graph Neural Networks: A Taxonomic Survey

Explainability in Graph Neural Networks: A Taxonomic Survey

We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.

R0: dde004c79ac901067ab1189ea01b8ac7-Data Science: A First Introduction

Data Science: A First Introduction

The book is structured so that learners spend the first four chapters learning how to use the R programming language and Jupyter notebooks to load, wrangle/clean, and visualize data, while answering descriptive and exploratory data analysis questions. The remaining chapters illustrate how to solve four common problems in data science, which are useful for answering predictive and inferential data analysis questions[…]

R0identifier_ 7029b309b114bdb1aa72b6fd0da905f3-Yann LeCun’s Deep Learning Course at CDS

Yann LeCun’s Deep Learning Course at CDS

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

R0_fe33488e78e8d3bac711f1ffb6ea5a48-Bayesian-Data-Analysis-course

Bayesian Data Analysis: book & course

This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics

Fourier Neural Operator for Parametric Partial Differential Equations

Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution.

R0_b6959f4e35b2c24097cf630fa7cea606-Evaluating and Characterizing Human Rationales

Evaluating and Characterizing Human Rationales

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.

->Artificial intelligence towards data science

Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.