This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
This document discusses DeepMind's AI system AlphaFold and its success in protein structure prediction. AlphaFold uses deep learning to predict protein structures from amino acid sequences alone, solving a 50-year grand challenge in biology. It achieved extremely accurate predictions in the CASP14 protein structure prediction competition. The development of AlphaFold and its impressive results were discussed in various articles and videos linked in the document.
This slides explain about scanning picture feature points that is made by SIFT(Scale Invariant Feature Transform) which uses Gaussian Filter Difference Logic (DoG).
This document summarizes scattering in computer graphics and computer vision, including:
- Types of scattering such as diffuse reflection, specular reflection, BRDF, subsurface scattering, single scattering, and multiple scattering.
- Models for subsurface scattering including diffuse approximation, plane-parallel approximation, and Donner's empirical BSSRDF model.
- Techniques for measuring scattering properties like BRDF and rendering effects of scattering in participating media and subsurface scattering.
This document discusses DeepMind's AI system AlphaFold and its success in protein structure prediction. AlphaFold uses deep learning to predict protein structures from amino acid sequences alone, solving a 50-year grand challenge in biology. It achieved extremely accurate predictions in the CASP14 protein structure prediction competition. The development of AlphaFold and its impressive results were discussed in various articles and videos linked in the document.
This slides explain about scanning picture feature points that is made by SIFT(Scale Invariant Feature Transform) which uses Gaussian Filter Difference Logic (DoG).
This document summarizes scattering in computer graphics and computer vision, including:
- Types of scattering such as diffuse reflection, specular reflection, BRDF, subsurface scattering, single scattering, and multiple scattering.
- Models for subsurface scattering including diffuse approximation, plane-parallel approximation, and Donner's empirical BSSRDF model.
- Techniques for measuring scattering properties like BRDF and rendering effects of scattering in participating media and subsurface scattering.
