Deep Generative Modeling
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI techniques.
Deep Generative Models - Lecture 5: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this comprehensive Stanford lecture.
Deep Generative Models - Lecture 6: Variational Autoencoders
Explore Variational Autoencoders (VAEs) in deep generative modeling, covering theoretical foundations and practical applications in this Stanford lecture by Prof. Stefano Ermon.
Multi-omics Variational Autoencoding - Deep Dive and Primer
Explore multi-omics variational autoencoding for integrating complex biological datasets. Learn about MOVE pipeline, data preprocessing, model optimization, and approaches for determining associations between omics variables and categorical labels.
Generative Models
Explore denoising autoencoders, VAEs, GANs, and RBMs in this comprehensive introduction to generative models, covering key concepts and applications in machine learning.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, variational autoencoders, and GANs. Learn about latent variable models, reparameterization tricks, and recent advances in generative AI.
Redes Neuronales de Grafos para la Prevención de Fraude
Explora el uso de grafos y redes neuronales para detectar phishing en banca empresarial, analizando patrones de conexión entre usuarios e IPs para identificar comportamientos sospechosos.
Weakly Supervised Causal Representation Learning
Explore weakly supervised causal representation learning, its theoretical foundations, and practical applications in disentangling causal variables from unstructured data like images for improved causal reasoning.
Exploring the Cosmos with Deep Learning
Explore how deep learning revolutionizes cosmology, from detecting gravitational lenses to generating galaxy images and modeling cosmic structures, addressing challenges in modern astronomical surveys.
Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning
Explore geometric deep learning techniques for object detection and segmentation in cryo-electron micrographs, focusing on unsupervised methods and graph-based transformers for complex structures.
Mixed Models in Genomic Analyses and Imaging Studies
Explore mixed models in genomics and imaging, focusing on efficient set tests, Gaussian process priors, gene-environment interactions, and neural networks for histologic feature quantification.
Classical and Deep Learning Approaches to Longitudinal Modeling in Neuroimages - CGSI 2024
Explore classical and deep learning methods for longitudinal modeling in neuroimaging, focusing on disease progression analysis and innovative techniques for tracking brain changes over time.
Variational Autoencoders and Performers in Deep Learning - Lecture 15
Delve into advanced deep learning concepts including Variational Autoencoders (VAE), variational inference, ELBO computation, and Performer-based Transformer architectures.
Variational Autoencoder From Scratch in PyTorch
Learn to implement a Variational Autoencoder from scratch using PyTorch, covering architecture, training loop, and inference with practical examples and code demonstrations.
NVAE- A Deep Hierarchical Variational Autoencoder
Detailed explanation of NVAE, a deep hierarchical variational autoencoder for high-resolution image generation, covering architecture, training techniques, and state-of-the-art results.
Neural Nets for NLP - Latent Variable Models
Explore latent variable models in NLP, covering variational autoencoders, discrete variables, and practical applications. Gain insights into generative modeling and advanced neural network techniques.
CMU Advanced NLP 2022 - Latent Variable Models
Explore advanced NLP concepts: latent variables, variational autoencoders, and their applications in natural language processing. Gain insights into generative models and feature learning.
Neural Nets for NLP 2021 - Multilingual Learning
Explore multilingual learning in NLP, covering models, data balancing, parameter sharing, and cross-lingual transfer techniques for improved language processing across diverse languages.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables for NLP, covering variational autoencoders, discrete latent variables, and their applications in language processing.
Neural Nets for NLP - Models with Latent Random Variables
Explore neural network models with latent random variables, covering variational autoencoders and their applications in NLP. Learn about generative vs. discriminative models and handling discrete latent variables.
Neural Nets for NLP - Latent Random Variables
Explore latent random variables in neural networks for NLP, covering variational autoencoders, discrete latent variables, and applications in language processing and text generation.