Reinforcement Learning for Materials Synthesis Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Reinforcement Learning for Materials Synthesis
Physics-constrained Computational Imaging Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Physics-constrained Computational Imaging
Session Panel: Generative Models Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Session Panel: Generative Models
Hybrid Physical - Deep Learning Models for Astronomical Inverse Problems Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Hybrid Physical - Deep Learning Models for Astronomical Inverse Problems
Towards a cosmology emulator using Generative Adversarial Networks Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Towards a cosmology emulator using Generative Adversarial Networks
Improved learning for materials and chemical structures through symmetry, hierarchy and similarity Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Improved learning for materials and chemical structures through symmetry, hierarchy and similarity
Deducing Inference from Hyperspectral Imaging of Materials Using Deep Recurrent Neural Networks Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Deducing Inference from Hyperspectral Imaging of Materials Using Deep Recurrent Neural Networks
Putting Non-Euclidean Geometry to Work in ML: Hyperbolic and Product Manifold Embeddings Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Putting Non-Euclidean Geometry to Work in ML: Hyperbolic and Product Manifold Embeddings
Flow-based generative models for lattice field theory Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Flow-based generative models for lattice field theory
Generative models as priors for signal denoising Presented at the Physics in Machine Learning Workshop held at BIDS May 29, 2019. Share this video Facebook LinkedIn Twitter Email Read more about Generative models as priors for signal denoising
