Low Distortion Embeddings With Bottom-up Manifold Learning
Explore bottom-up manifold learning techniques for creating low distortion embeddings, enhancing data visualization and dimensionality reduction in signal processing applications.
Tensor Network for Machine Learning Applications - Lecture 3
Explore advanced tensor network techniques for machine learning, focusing on applications and practical implementations in data analysis and modeling.
Tensor Network for Machine Learning Applications - Part 2
Explore advanced tensor network techniques for machine learning, focusing on applications and recent developments in the field.
Tensor Network for Machine Learning Applications - Lecture 1
Explore tensor networks' applications in machine learning, focusing on their potential to enhance computational efficiency and model complexity.
Supervised Quantile Normalization for Matrix Factorization Using Optimal Transport
Explore advanced matrix factorization techniques using optimal transport for quantile normalization. Learn applications in dimensionality reduction and genomics from expert Marco Cuturi.
Singular Value Decomposition and Rank-K Approximation - Lecture 19
Dive into Singular Value Decomposition (SVD) concepts, exploring vector path tracing and rank-k approximation techniques for advanced matrix analysis and dimensionality reduction.
Dimensionality Reduction: Rank-k Approximation and Eigen-decomposition - Lecture 20
Dive into advanced dimensionality reduction techniques, exploring rank-k approximation, eigen-decomposition, and power method applications in data analysis.
Dimensionality Reduction: Principal Component Analysis and Multidimensional Scaling - Lecture 21
Master dimensionality reduction techniques through Principal Component Analysis (PCA) and Multidimensional Scaling (MDS), exploring their applications in data analysis and visualization.
Data Mining: Frequent Directions and Random Projections - Spring 2023
Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.
Multidimensional Scaling and Linear Discriminant Analysis in Data Mining - Spring 2023
Explore multidimensional scaling (MDS) and linear discriminant analysis (LDA), learning key concepts, motivations, and practical applications in data dimensionality reduction and classification.
Matrix Completion and PageRank in Data Mining - Spring 2023
Dive into matrix completion techniques and PageRank algorithms, exploring fundamental concepts for data mining and graph analysis applications.
Networks that Adapt to Intrinsic Dimensionality Beyond the Domain
Explore neural networks' ability to adapt to intrinsic dimensionality, focusing on ReLU networks approximating functions with dimensionality-reducing feature maps. Gain insights into manifold learning and data analysis.
Probabilistic Methods for Data-Driven Reduced-Order Modeling
Explore probabilistic methods for data-driven reduced-order modeling, including Gaussian process regression, deep kernel learning, and Bayesian reduced-order operator inference for efficient multi-query simulations and digital twinning.
Higher-Order Multi-Variate Statistics for Scientific Data Analysis
Explore advanced multi-variate statistical techniques for scientific data analysis, focusing on higher-order methods like coskewness and cokurtosis for rare event detection and dimensionality reduction in complex systems.
Tensor Networks vs. PCA and PLS for High Dimensional Medical Datasets
Explore dimensionality reduction techniques for high-dimensional medical data. Compare tensor networks with PCA and PLS, gaining insights on their applications and effectiveness in handling complex datasets.
An Implementation of Sub-Space Model Based Automatic Speech Recognition - 2009
Explore advanced speech recognition techniques using sub-space models in this in-depth implementation guide from Johns Hopkins University's CLSP.
Discovery in High Dimensions - 2008
Explore statistical methods for uncovering hidden structures in high-dimensional data from various fields, including genetics, astronomy, and consumer behavior.
Spectral Learning
Explore spectral learning techniques and their applications in machine learning and data analysis with Lorenzo Chicchi's comprehensive lecture.
Navigating, Restructuring and Reshaping Learned Latent Spaces
Explore advanced techniques for manipulating learned latent spaces in machine learning, focusing on navigation, restructuring, and reshaping methods for improved model performance and efficiency.
Exploiting Compositional Structure in Deep Learning - Theory and Applications
Delve into high-dimensional function modeling and deep learning applications for heart digital twins, exploring theoretical frameworks and practical implementations in medical risk prediction.