The main objective of Torchdyn is to provide a centralized hub for layers, numerical routines and utilities required for high-performance implementation of numerical deep learning models
By providing a centralized, easy-to-access collection of model templates, tutorial and application notebooks, we hope to speed-up research in numerical deep learning and provide a hub of reusable computational primitives and numerical routines.
Torchdyn leverages modern PyTorch best practices and handles training with pytorch-lightning. We build Graph Neural ODEs utilizing the Graph Neural Networks (GNNs) API of dgl. For a complete list of references, check pyproject.toml. We offer a suite of ODE solvers, sensitivity methods, root finding algorithms and other common utilities for numerical deep learning.
This video provides an introduction to key concepts and potential applications.
Interest in the blend of differential equations, deep learning and dynamical systems has been reignited by recent works
We explore how differentiable programming can unlock the effectiveness of deep learning to accelerate progress across scientific domains, including control, fluid dynamics and in general prediction of complex dynamical systems. Conversely, we focus on models powered by numerical methods and signal processing to advance the state of AI in classical domains such as vision of natural language.
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Who we are
Postdoctoral Research Fellow at Mila - Quebec under Prof. Yoshua Bengio. Co-founder of @DiffeqML and Syntensor contributor. Interested in dynamical systems, deep learning, optimization and control.
C. S. PhD Student at Stanford. Co-founder of @DiffeqML and Syntensor contributor. Working at the intersection of deep learning, generative models and numerical optimization.
Cofounder and CEO/CTO at Syntensor. Applying geometric deep learning models and neural differential equations to biology, modeled as a dynamical system at the ‘edge of chaos’.
Founding Machine Learning Engineer at Syntensor. Working on fundamental methods development in numerical solving suites and multi-scale dynamical systems.
Work with us
We’re hiring! Syntensor is combining methods in geometric deep learning with stochastic neural graph differential equations to model dynamic biological systems at scale. We believe their work is the most advanced commercial application of Torchdyn at scale. If this sounds intriguing, please send your resumé to email@example.com - speak soon!