Machine Learning and Deep Learning Maths - Matrix and Vector Operations
Explore essential matrix and vector operations for machine learning and deep learning using Python, covering creation, manipulation, and mathematical computations.
Structure and Matrices in Julia Programming - Lecture 3
Explore matrices, sparse structures, and SVD for image compression and computational thinking in Julia, with practical applications in machine learning and linear algebra.
Sparse Matrices in Sparse Analysis - Anna Gilbert
Explore sparse matrices in compressive sensing, focusing on expander graphs and their applications. Learn about metric repair scenarios and algorithms for sparse signal recovery.
FSSpMDM - Accelerating Small Sparse Matrix Multiplications by Run-Time Code Generation
Explore code generation strategies for accelerating small sparse matrix multiplications in high-order finite element solvers, focusing on performant operator kernels for prismatic and hexahedral elements.
Spectrum of Sparse Inhomogeneous Random Graphs
Explore the spectrum of sparse inhomogeneous random graphs, examining structural properties and applications in network science, probability theory, and statistical physics.
PDEs and Stencils - SPARSE 2024
Explore PDEs and stencils in computational science, focusing on efficient numerical methods for solving partial differential equations.
Exploiting Sparsity and Redundancy - SPARSE 2024
Explore techniques for leveraging sparsity and redundancy in data structures and algorithms to optimize performance and resource utilization.
Practical Quantum Circuits for Block Encodings of Sparse Matrices
Explore efficient quantum circuits for block encodings of sparse matrices, focusing on strategies for well-structured matrices and quantum walks on graphs. Gain insights into quantum linear algebra algorithms.
Coded Matrix Computation: Numerical Stability, Partial Stragglers, and Sparse Input Matrices
Explore advanced techniques for high-dimensional matrix computations in distributed systems, addressing numerical stability, partial stragglers, and sparse input matrices.
Polar Codes for IDS Channels
Explore polar codes for IDS channels, addressing numerical instability, partial stragglers, and worker computation time in sparse matrix scenarios. Learn recent contributions to overcome these challenges.
C++ Compile-Time Sparse Matrices for Linear Algebra and Tracking Applications
Explore compile-time sparse matrices in C++ for efficient linear algebra and tracking applications, optimizing memory usage and performance through advanced type system techniques.
Improved Approximations for Extremal Eigenvalues of Sparse Hamiltonians
Discover advanced approximation techniques for eigenvalues in sparse Hamiltonians, exploring new methods for quantum systems with q-local terms and improved mathematical approaches for k-sparse configurations.
Parallel Algebraic Multigrid in Julia with PartitionedArrays.jl - JuliaCon 2024
Explore parallel algebraic multigrid methods in Julia using PartitionedArrays.jl. Learn about recent updates, implementation strategies, and advantages over traditional MPI-based approaches for large-scale linear solvers.
Building Decision-Making Assistants: Introduction to Recommendation Systems
Explore recommendation systems from basic classification to advanced Netflix-style matrix factorization, covering content filtering, collaboration models, SVD, and PCA techniques.
Compile-Time Sparse Matrices - Optimizing Memory and Performance
Explore compile-time sparse matrices for efficient memory use and performance in C++ linear algebra. Learn to incorporate sparseness information, reduce storage, and optimize operations automatically.