Generalized Abs-Linear Learning
14. prosince 2019
Řečníci
Andreas Griewank
Řečník · 0 sledujících
O prezentaci
Machine learning researchers often express complex models as a program, relying on program transformations to add functionality. New languages and transformations (e.g., TorchScript and TensorFlow AutoGraph) are becoming core capabilities of ML libraries. However, existing transformations, such as automatic differentiation (AD or autodiff), inference in probabilistic programming languages (PPLs), and optimizing compilers are often built in isolation, and limited in scope. This workshop aims at viewing program transformations in ML in a unified light, making these capabilities more accessible, and building entirely new ones. Program transformations are an area of active study. AD transforms a program performing numerical computation into one computing the gradient of those computations. In probabilistic programming, a program describing a sampling procedure can be modified to perform inference on model parameters given observations. Other examples are vectorizing a program expressed on one data point, and learned transformations where ML models use programs as inputs or outputs. This workshop will bring together researchers in the fields of AD, probabilistic programming, programming languages, compilers, and ML, with the goal of understanding the commonalities between disparate approaches and views, and sharing ways to make these techniques broadly available. It would enable ML practitioners to iterate faster on novel models and architectures (e.g., those naturally expressed through high-level constructs like recursion).Machine learning researchers often express complex models as a program, relying on program transformations to add functionality. New languages and transformations (e.g., TorchScript and TensorFlow AutoGraph) are becoming core capabilities of ML libraries. However, existing transformations, such as automatic differentiation (AD or autodiff), inference in probabilistic programming languages (PPLs), and optimizing compilers are often built in isolation, and limited in scope. This workshop aims at v…
Kategorie
Matematika
Kategorie · 2,4k prezentací
Umělá inteligence a data science
Kategorie · 10,8k prezentací
O organizátorovi (NIPS 2019)
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
Baví vás formát? Nechte SlidesLive zachytit svou akci!
Profesionální natáčení a streamování po celém světě.
Sdílení
Doporučená videa
Prezentace na podobné téma, kategorii nebo přednášejícího
Emotion recognition using Texture Maps and Convolutional Neural Networks
Zhlédnout později
AI Driving Olympics 3
Zhlédnout později
Invited talk: Scalable Robot Learning in Complex Enviroments
Zhlédnout později
Panel Discussion
Zhlédnout později
Invited talk: Finding the right Balance of Modelling and Learning for Vision-based Robot Manipulation
Zhlédnout později
